diff --git a/news/index.html b/news/index.html
index 8d3b2f0..e8dc578 100644
--- a/news/index.html
+++ b/news/index.html
@@ -59,7 +59,7 @@ <h2 class="pkg-version" data-toc-text="0.0.2" id="version-002">version 0.0.2<a c
 <p>Fixing a minor bug discovered by the CRAN checks.</p>
 </div>
     <div class="section level2">
-<h2 class="pkg-version" data-toc-text="0.0.3" id="version-003">version 0.0.3<a class="anchor" aria-label="anchor" href="#version-003"></a></h2>
+<h2 class="pkg-version" data-toc-text="0.0.3" id="version-003">version 0.0.3<a class="anchor" aria-label="anchor" href="#version-003"></a></h2><p class="text-muted">CRAN release: 2024-03-01</p>
 <p>Fixing a bug related to the summary for grouping variables. Removed the export tag from a few internal functions for a cleaner reference manual, and added the pkgdown site.</p>
 </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/pkgdown.yml b/pkgdown.yml
index 2b1b0ad..e80a036 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -3,7 +3,7 @@ pkgdown: 2.0.7
 pkgdown_sha: ~
 articles:
   Disproportionality_analysis: Disproportionality_analysis.html
-last_built: 2024-03-01T19:52Z
+last_built: 2024-03-04T10:44Z
 urls:
   reference: https://oskargauffin.github.io/pvutils/reference
   article: https://oskargauffin.github.io/pvutils/articles
diff --git a/search.json b/search.json
index 30e5c6a..cb9d6ec 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
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Conveying Verbatim Copies","title":"GNU General Public License","text":"may convey verbatim copies Program’s source code receive , medium, provided conspicuously appropriately publish copy appropriate copyright notice; keep intact notices stating License non-permissive terms added accord section 7 apply code; keep intact notices absence warranty; give recipients copy License along Program. may charge price price copy convey, may offer support warranty protection fee.","code":""},{"path":"https://oskargauffin.github.io/pvutils/LICENSE.html","id":"id_5-conveying-modified-source-versions","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"5. Conveying Modified Source Versions","title":"GNU General Public License","text":"may convey work based Program, modifications produce Program, form source code terms section 4, provided also meet conditions: ) work must carry prominent notices stating modified , giving relevant date. b) work must carry prominent notices stating released License conditions added section 7. requirement modifies requirement section 4 “keep intact notices”. c) must license entire work, whole, License anyone comes possession copy. License therefore apply, along applicable section 7 additional terms, whole work, parts, regardless packaged. 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Conveying Non-Source Forms","title":"GNU General Public License","text":"may convey covered work object code form terms sections 4 5, provided also convey machine-readable Corresponding Source terms License, one ways: ) Convey object code , embodied , physical product (including physical distribution medium), accompanied Corresponding Source fixed durable physical medium customarily used software interchange. b) Convey object code , embodied , physical product (including physical distribution medium), accompanied written offer, valid least three years valid long offer spare parts customer support product model, give anyone possesses object code either (1) copy Corresponding Source software product covered License, durable physical medium customarily used software interchange, price reasonable cost physically performing conveying source, (2) access copy Corresponding Source network server charge. c) Convey individual copies object code copy written offer provide Corresponding Source. alternative allowed occasionally noncommercially, received object code offer, accord subsection 6b. d) Convey object code offering access designated place (gratis charge), offer equivalent access Corresponding Source way place charge. need require recipients copy Corresponding Source along object code. place copy object code network server, Corresponding Source may different server (operated third party) supports equivalent copying facilities, provided maintain clear directions next object code saying find Corresponding Source. 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Termination","title":"GNU General Public License","text":"may propagate modify covered work except expressly provided License. attempt otherwise propagate modify void, automatically terminate rights License (including patent licenses granted third paragraph section 11). However, cease violation License, license particular copyright holder reinstated () provisionally, unless copyright holder explicitly finally terminates license, (b) permanently, copyright holder fails notify violation reasonable means prior 60 days cessation. Moreover, license particular copyright holder reinstated permanently copyright holder notifies violation reasonable means, first time received notice violation License (work) copyright holder, cure violation prior 30 days receipt notice. 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Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"https://oskargauffin.github.io/pvutils/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least “copyright” line pointer full notice found. Also add information contact electronic paper mail. program terminal interaction, make output short notice like starts interactive mode: hypothetical commands show w show c show appropriate parts General Public License. course, program’s commands might different; GUI interface, use “box”. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU GPL, see <http://www.gnu.org/licenses/>. GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read <http://www.gnu.org/philosophy/--lgpl.html>.","code":"<one line to give the program's name and a brief idea of what it does.> Copyright (C) <year>  <name of author>  This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.  This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.  You should have received a copy of the GNU General Public License along with this program.  If not, see <http://www.gnu.org/licenses/>. <program>  Copyright (C) <year>  <name of author> This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Disproportionality analysis","text":"life cycle medicinal product, knowledge safety profile continues accumulate post market introduction. Adverse event reports collected spontaneous reporting play instrumental part development. Adverse event report databases often cover wide range drugs events, beyond can feasibly monitored manual work. Disproportionality analysis thus often used highlight drug-event-combinations potential interest qualitative manual case--case assessment. pvda package provides three disproportionality estimators: proportional reporting rate (PRR), reporting odds ratio (ROR) information component (IC). takes report level data input, outputs estimates drug-event-combination, possibly stratified variable interest, e.g. age group /sex.","code":""},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"running-a-disproportionality-analysis","dir":"Articles","previous_headings":"","what":"Running a disproportionality analysis","title":"Disproportionality analysis","text":"workhorse package function da. da-function takes patient level data data frame input (drug_event_df) produces estimates summary counts demonstrated . drug_event_df simulated data set drugs named Drug_A - Drug_Z events Event_1 - Event_1000. details simulation documented data object (execute ?df_drug_event).","code":"da_1 <-    drug_event_df |>   da()"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"specifying-the-column-names","dir":"Articles","previous_headings":"Running a disproportionality analysis","what":"Specifying the column names","title":"Disproportionality analysis","text":"da-function examine passed data set column names contained passed parameter list “df_colnames”. default column names “report_id”, “drug” “event”. rename report-id column “drug_event_df” need specify new name call da, shown :","code":"da_1 <-   drug_event_df |>    dplyr::rename(reportID = report_id) |>    da(df_colnames = list(report_id = \"reportID\"))"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"summarizing-and-printing-results","dir":"Articles","previous_headings":"Running a disproportionality analysis","what":"Summarizing and printing results","title":"Disproportionality analysis","text":"‘da_1’ object can passed summary print function.   (Technical note: printing colors console easily reproduced within vignette , provide screenshots applicable. )","code":"summary(da_1) print(da_1, n=3)"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"passing-a-correctly-structured-input-data","dir":"Articles","previous_headings":"Running a disproportionality analysis","what":"Passing a correctly structured input data","title":"Disproportionality analysis","text":"input data needs report-level format. every line data frame corresponds drug-event pair specific report. drug_event_df-example data, first three rows drug_event_df report, report_id = 1. first row reports Drug_D adverse event named Event_5.next three rows another report, instance drug B reported two different events, event 15 event 33. can ignore group-column now.","code":"drug_event_df[1:6, ] ## # A tibble: 6 × 4 ##   report_id drug   event    group ##       <int> <chr>  <chr>    <dbl> ## 1         1 Drug_A Event_23     1 ## 2         1 Drug_D Event_6      1 ## 3         1 Drug_E Event_8      1 ## 4         1 Drug_I Event_9      1 ## 5         2 Drug_B Event_2      0 ## 6         2 Drug_G Event_13     0"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"extracting-the-results","dir":"Articles","previous_headings":"Running a disproportionality analysis","what":"Extracting the results","title":"Disproportionality analysis","text":"covenience, output object da list class “da” containing two objects, results (“da_df”) input parameters (“input_params”). point might want extract results input parameters list, demonstrate alternatives:","code":"names(da_1) ## [1] \"da_df\"        \"input_params\" # Different ways of extracting the results data frame # da_1$da_df # da_1[['da_df']] # da_1 |> purrr::pluck(\"da_df\")"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"how-to-run-a-subgroup-analysis","dir":"Articles","previous_headings":"Running a disproportionality analysis","what":"How to run a subgroup analysis?","title":"Disproportionality analysis","text":"da function builds comparator passed data, e.g. prr excluding drug interest comparator, sometimes referred background. implies subgroup analysis e.g. specific age groups can achieved creating new data frame subgroup interest, passing da. exhaustive grouping required, one can provide grouping variable group_by da. executes call da grouping return results single data frame. grouping instance age groups, sex, countries, regions combination thereof, needs passed single variable (character numeric). drug_event_df contains two groups, column named “group”. can pass “group” group_by parameter df_colnames . output note third column “group”, .e. column name passed. also note drug-event-pairs kept together, e.g. first second row contains drug-event-combination, “Drug_Z” “Event_1”. simplify comparisons within drug-event-pair across different groups, e.g. pattern seen across males females. ordering drug-events output made averaging selected disproportionality estimate (parameter sort_by), .e. top two rows highest average “ic2.5”.","code":"drug_event_df |>    head() ## # A tibble: 6 × 4 ##   report_id drug   event    group ##       <int> <chr>  <chr>    <dbl> ## 1         1 Drug_A Event_23     1 ## 2         1 Drug_D Event_6      1 ## 3         1 Drug_E Event_8      1 ## 4         1 Drug_I Event_9      1 ## 5         2 Drug_B Event_2      0 ## 6         2 Drug_G Event_13     0 da_grouped <-    drug_event_df |>    da(df_colnames = list(group_by=\"group\"))   print(da_grouped$da_df) ## # A tibble: 1,808 × 24 ##    drug   event   group   obs exp_rrr ic2.5    ic ic97.5 exp_prr prr2.5   prr ##    <chr>  <chr>   <dbl> <int>   <dbl> <dbl> <dbl>  <dbl>   <dbl>  <dbl> <dbl> ##  1 Drug_Z Event_1     0    45    26.5  0.3   0.75   1.14    23.4   1.54  1.92 ##  2 Drug_Z Event_1     1    43    29.1  0.09  0.56   0.96    26.5   1.28  1.63 ##  3 Drug_Z Event_2     0    33    24.6 -0.12  0.42   0.87    23.2   1.07  1.42 ##  4 Drug_Z Event_2     1    42    27.8  0.12  0.59   0.99    25.1   1.3   1.67 ##  5 Drug_V Event_2     0    34    26.0 -0.14  0.38   0.82    24.6   1.04  1.38 ##  6 Drug_V Event_2     1    46    31.0  0.12  0.56   0.95    27.8   1.3   1.66 ##  7 Drug_V Event_1     0    40    28.0  0.03  0.51   0.92    25.8   1.21  1.55 ##  8 Drug_V Event_1     1    42    32.4 -0.1   0.37   0.77    30.3   1.07  1.38 ##  9 Drug_U Event_2     0    42    31.5 -0.06  0.41   0.81    29.1   1.11  1.44 ## 10 Drug_U Event_2     1    37    26.8 -0.04  0.46   0.89    24.9   1.14  1.49 ## # ℹ 1,798 more rows ## # ℹ 13 more variables: prr97.5 <dbl>, exp_ror <dbl>, ror2.5 <dbl>, ror <dbl>, ## #   ror97.5 <dbl>, n_drug <int>, n_event <int>, n_tot <int>, n_event_prr <int>, ## #   n_tot_prr <int>, b <int>, c <int>, d <int>"},{"path":[]},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"how-are-drug-event-pairs-occurrings-several-times-in-one-report-counted","dir":"Articles","previous_headings":"Frequently Asked Questions","what":"How are drug-event pairs occurrings several times in one report counted?","title":"Disproportionality analysis","text":"drug-event pair occurs several times one report, contributes counts . , observed count five means five different reports containing drug-event-pair (least ). due single report containing drug-event pair five times due repeated events data quality issues. demonstrated , duplicate first row drug_event_df still arrive observed count 1.","code":"first_row <- drug_event_df[1,]  first_row |>    dplyr::bind_rows(first_row) |>    da() |>    purrr::pluck(\"da_df\") |>    dplyr::pull(obs) ## [1] 1"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"how-is-the-expected-calculated","dir":"Articles","previous_headings":"Frequently Asked Questions","what":"How is the expected calculated?","title":"Disproportionality analysis","text":"Detailed formulas PRR, RRR IC documented within function ( “?prr”, “?rrr”, “?ic”), overall one can note expected counts derived contingency table exposed drug, experience adverse event. sample used contingency table data frame passed da function. See also grouping parameter.","code":""},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"where-is-exp_ic-in-the-output-or-what-is-exp_prr","dir":"Articles","previous_headings":"Frequently Asked Questions","what":"Where is exp_ic in the output, or what is exp_prr?","title":"Disproportionality analysis","text":"common question regards output column names, ic preceded “exp_prr”. information component uses expected count Relative Reporting Rate (RRR), perhaps well known pharmacovigilande community.","code":"# Note the fourth column name: drug_event_df |>    da() |>    purrr::pluck(\"da_df\") |>    dplyr::select(drug:ic97.5) |>    print(n=1) ## # A tibble: 1,306 × 7 ##   drug   event     obs exp_rrr ic2.5    ic ic97.5 ##   <chr>  <chr>   <int>   <dbl> <dbl> <dbl>  <dbl> ## 1 Drug_Z Event_1    88    55.6  0.34  0.66   0.94 ## # ℹ 1,305 more rows"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"rule-of-n","dir":"Articles","previous_headings":"Frequently Asked Questions","what":"Rule of N","title":"Disproportionality analysis","text":"function da several input parameters. One rule_of_N, default set 3, sometimes referred “rule three”. sets ROR PRR-values NA observed count less specified N. completeness, note default shrinkage IC acts built ‘rule 3’, (.e. shrinkage +0.5 prevents lower bound exceed 0 default significance level 95%).","code":""},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"controlling-the-number-of-digits","dir":"Articles","previous_headings":"Frequently Asked Questions","what":"Controlling the number of digits","title":"Disproportionality analysis","text":"function da parameter number_of_digits controls rounding non-count values output, including expected counts, uncertainty bounds point estimates.","code":"drug_event_df |>   da(     rule_of_N = 3,     number_of_digits = 8   ) |> print(n = 3)"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"can-i-use-pvda-to-get-ic-prr-or-ror-when-i-already-have-the-observed-and-expected-counts","dir":"Articles","previous_headings":"Frequently Asked Questions","what":"Can I use pvda to get IC, PRR or ROR, when I already have the observed and expected counts?","title":"Disproportionality analysis","text":"Yes, can calculate point estimates confidence credibility intervals specific drug-event-combination already number observed reports, required counts database total count, number reports exposure number reports event. can helpful instance quality check results, e.g. papers software. details specific input parameters, consult documentation function.","code":"prr(obs = 10, n_drug = 1000, n_event_prr = 200, n_tot_prr = 10000) ## # A tibble: 1 × 3 ##   prr2.5   prr prr97.5 ##    <dbl> <dbl>   <dbl> ## 1  0.266   0.5   0.940 ror(a = 10, b = 20, c = 200, d = 10000) ## # A tibble: 1 × 3 ##   ror2.5   ror ror97.5 ##    <dbl> <dbl>   <dbl> ## 1   11.6    25    54.1 ic(obs = 10, exp = 5) ## # A tibble: 1 × 3 ##     ic2.5    ic ic97.5 ##     <dbl> <dbl>  <dbl> ## 1 -0.0973 0.933   1.69"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"how-fast-is-pvda","dir":"Articles","previous_headings":"Frequently Asked Questions","what":"How fast is pvda?","title":"Disproportionality analysis","text":"data.table-package (“dtplyr”) used fast execution. test execution speed made using vaers R package, available gitlab. vaers package contains data VAERS years 1990 - 2018, resulting 4 146 778 rows processed da pvda. Execution time regular laptop less 10 seconds.","code":""},{"path":"https://oskargauffin.github.io/pvutils/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Oskar Gauffin. Author, maintainer.","code":""},{"path":"https://oskargauffin.github.io/pvutils/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Gauffin O (2024). pvda: Disproportionality Functions Pharmacovigilance. R package version 0.0.3, https://oskargauffin.github.io/pvda/.","code":"@Manual{,   title = {pvda: Disproportionality Functions for Pharmacovigilance},   author = {Oskar Gauffin},   year = {2024},   note = {R package version 0.0.3},   url = {https://oskargauffin.github.io/pvda/}, }"},{"path":"https://oskargauffin.github.io/pvutils/index.html","id":"pvda-","dir":"","previous_headings":"","what":"Disproportionality Functions for Pharmacovigilance","title":"Disproportionality Functions for Pharmacovigilance","text":"R package executing disproportionality analyses pharmacovigilance, using information component (IC), proportional reporting rate (PRR) reporting odds ratio (ROR).","code":""},{"path":"https://oskargauffin.github.io/pvutils/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Disproportionality Functions for Pharmacovigilance","text":"","code":"# Install stable version from CRAN  install.packages(\"pvda\")"},{"path":"https://oskargauffin.github.io/pvutils/index.html","id":"example-code","dir":"","previous_headings":"","what":"Example code","title":"Disproportionality Functions for Pharmacovigilance","text":"run disproportionality analysis, pass adverse event report-level data (, drug_event_df) function da : extract results data frame, access “da_df” list object:","code":"library(\"pvda\")  da1 <-  drug_event_df |>  da()  summary(da1) da_results <-  da1$da_df"},{"path":"https://oskargauffin.github.io/pvutils/reference/add_disproportionality.html","id":null,"dir":"Reference","previous_headings":"","what":"Add disproportionality estimates to data frame\nwith expected counts — add_disproportionality","title":"Add disproportionality estimates to data frame\nwith expected counts — add_disproportionality","text":"Add disproportionality estimates data frame expected counts","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/add_disproportionality.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add disproportionality estimates to data frame\nwith expected counts — add_disproportionality","text":"","code":"add_disproportionality(   df = NULL,   df_syms = NULL,   da_estimators = c(\"ic\", \"prr\", \"ror\"),   rule_of_N = 3,   conf_lvl = 0.95 )"},{"path":"https://oskargauffin.github.io/pvutils/reference/add_disproportionality.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add disproportionality estimates to data frame\nwith expected counts — add_disproportionality","text":"df Intended use output tibble add_expected_counts. df_syms list built df_colnames conversion symbols. da_estimators Character vector specifying disproportionality estimators use, case need implemented options. Defaults c(\"ic\", \"prr\", \"ror\"). rule_of_N Numeric value. Sets estimates ROR PRR NA observed counts strictly less passed value rule_of_N. Default value 3, 5 sometimes used liberal alternative. Set NULL want apply rule. conf_lvl Confidence level confidence credibility intervals. Default 0.95 (.e. 95 % confidence interval).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/add_disproportionality.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add disproportionality estimates to data frame\nwith expected counts — add_disproportionality","text":"passed data frame disproportionality point interval estimates.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/add_expected_counts.html","id":null,"dir":"Reference","previous_headings":"","what":"Produces expected counts — add_expected_counts","title":"Produces expected counts — add_expected_counts","text":"Produces various counts used disproportionality analysis.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/add_expected_counts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Produces expected counts — add_expected_counts","text":"","code":"add_expected_counts(   df = NULL,   df_colnames = NULL,   df_syms = NULL,   expected_count_estimators = c(\"rrr\", \"prr\", \"ror\") )"},{"path":"https://oskargauffin.github.io/pvutils/reference/add_expected_counts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Produces expected counts — add_expected_counts","text":"df object possible convert data table, e.g. tibble data.frame, containing patient level reported drug-event-pairs. See header 'df object' details. df_colnames list column names use df. , point da 'report id'-column (report_id), 'drug name'-column (drug), 'adverse event'-column (event) optionally grouping column group_by calculate disproportionality across. See vignette details. df_syms list built df_colnames conversion symbols. expected_count_estimators character vector containing desired expected count estimators. Defaults c(\"rrr\", \"prr\", \"ror\").","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/add_expected_counts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Produces expected counts — add_expected_counts","text":"tibble containing various counts.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/add_expected_counts.html","id":"the-df-object","dir":"Reference","previous_headings":"","what":"The df object","title":"Produces expected counts — add_expected_counts","text":"passed df (convertible ) data table least contain three  columns: report_id, drug event. data table contain one row  per reported drug-event-combination, .e. receiving single additional report  drug X event Y add one row table. single report  contained drug X event Y event Z, two rows added,  report_id drug rows. Column report_id must type  numeric character. Columns drug event must type character.  column group_by provided, can either numeric character.  can use df column names choosing, long  connect role name df_colnames-parameter.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/apply_rule_of_N.html","id":null,"dir":"Reference","previous_headings":"","what":"apply_rule_of_N — apply_rule_of_N","title":"apply_rule_of_N — apply_rule_of_N","text":"Internal function set disproportionality cells ROR PRR NA observed count < 3","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/apply_rule_of_N.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"apply_rule_of_N — apply_rule_of_N","text":"","code":"apply_rule_of_N(   da_df = NULL,   da_estimators = c(\"ic\", \"prr\", \"ror\"),   rule_of_N = NULL )"},{"path":"https://oskargauffin.github.io/pvutils/reference/apply_rule_of_N.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"apply_rule_of_N — apply_rule_of_N","text":"da_df See intermediate object da_df add_disproportionality da_estimators Default c(\"ic\", \"prr\", \"ror\"). rule_of_N length one integer 0 10.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/apply_rule_of_N.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"apply_rule_of_N — apply_rule_of_N","text":"input data frame (da_df) potentially cells set NA.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/apply_rule_of_N.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"apply_rule_of_N — apply_rule_of_N","text":"Sometimes, want protect spurious findings based small observed counts combined infinitesimal expected counts.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/build_colnames_da.html","id":null,"dir":"Reference","previous_headings":"","what":"An internal function creating colnames for da confidence/credibility bounds — build_colnames_da","title":"An internal function creating colnames for da confidence/credibility bounds — build_colnames_da","text":"Given output quantile_prob, da_name string, create column names PRR025, ROR025 IC025","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/build_colnames_da.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"An internal function creating colnames for da confidence/credibility bounds — build_colnames_da","text":"","code":"build_colnames_da(   quantile_prob = list(lower = 0.025, upper = 0.975),   da_name = NULL )"},{"path":"https://oskargauffin.github.io/pvutils/reference/build_colnames_da.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"An internal function creating colnames for da confidence/credibility bounds — build_colnames_da","text":"quantile_prob list two parameters, lower upper. Default: list(lower = 0.025, upper = 0.975) da_name string, \"ic\", \"prr\" \"ror\". Default: NULL","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/build_colnames_da.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"An internal function creating colnames for da confidence/credibility bounds — build_colnames_da","text":"list two symbols, inserted dtplyr-chain","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ic.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for Information Component (IC) — ci_for_ic","title":"Confidence intervals for Information Component (IC) — ci_for_ic","text":"Mainly used function ic. Produces quantiles posterior gamma distribution. Called twice ic create credibility intervals.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for Information Component (IC) — ci_for_ic","text":"","code":"ci_for_ic(obs, exp, conf_lvl_probs, shrinkage)"},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for Information Component (IC) — ci_for_ic","text":"obs numeric vector observed counts, .e. number reports selected drug-event-combination. Note shrinkage (e.g. +0.5) added inside function included . exp numeric vector expected counts, .e. number reports expected given comparator background. Note shrinkage (e.g. +0.5) added inside function included . conf_lvl_probs probabilities posterior, based passed confidence level (conf_lvl) ic. instance, sgn_lvl = .95 ic used, quantiles extracted sgn_lvl_probs 0.025 0.975. shrinkage non-negative numeric value, added observed expected count. Default 0.5.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for Information Component (IC) — ci_for_ic","text":"credibility interval specified input parameters.","code":""},{"path":[]},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_prr.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for Proportional Reporting Rate — ci_for_prr","title":"Confidence intervals for Proportional Reporting Rate — ci_for_prr","text":"Mainly use prr. Produces (symmetric, normality based) confidence bounds PRR, passed probability. Called twice prr create confidence intervals.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_prr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for Proportional Reporting Rate — ci_for_prr","text":"","code":"ci_for_prr(   obs = NULL,   n_drug = NULL,   n_event_prr = NULL,   n_tot_prr = NULL,   conf_lvl_probs = 0.95 )"},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_prr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for Proportional Reporting Rate — ci_for_prr","text":"obs Number reports specific drug event (.e. observed count). n_drug Number reports drug interest. n_event_prr Number reports event background. n_tot_prr Number reports background. conf_lvl_probs probabilities normal distribution, based passed confidence level (conf_lvl) prr. sgn_lvl = .95 prr, quantiles normal distribution extracted sgn_lvl_probs 0.025 0.975.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_prr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for Proportional Reporting Rate — ci_for_prr","text":"confidence interval specified input parameters.","code":""},{"path":[]},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ror.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for Reporting Odds Ratio — ci_for_ror","title":"Confidence intervals for Reporting Odds Ratio — ci_for_ror","text":"Mainly use ror. Produces (symmetric, normality based) confidence bounds ROR, passed probability. Called twice ror create confidence intervals.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ror.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for Reporting Odds Ratio — ci_for_ror","text":"","code":"ci_for_ror(a, b, c, d, conf_lvl_probs)"},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ror.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for Reporting Odds Ratio — ci_for_ror","text":"Number reports specific drug event (.e. observed count). b Number reports drug, without event c Number reports without drug, event d Number reports without drug, without event conf_lvl_probs probabilities normal distribution, based passed confidence level (conf_lvl) ror. sgn_lvl = .95 ror, quantiles normal distribution extracted sgn_lvl_probs 0.025 0.975.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ror.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for Reporting Odds Ratio — ci_for_ror","text":"credibility interval specified input parameters.","code":""},{"path":[]},{"path":"https://oskargauffin.github.io/pvutils/reference/conf_lvl_to_quantile_prob.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile probabilities from confidence level — conf_lvl_to_quantile_prob","title":"Quantile probabilities from confidence level — conf_lvl_to_quantile_prob","text":"Calculates equi-tailed quantile probabilities confidence level","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/conf_lvl_to_quantile_prob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile probabilities from confidence level — conf_lvl_to_quantile_prob","text":"","code":"conf_lvl_to_quantile_prob(conf_lvl = 0.95)"},{"path":"https://oskargauffin.github.io/pvutils/reference/conf_lvl_to_quantile_prob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile probabilities from confidence level — conf_lvl_to_quantile_prob","text":"conf_lvl Confidence level confidence credibility intervals. Default 0.95 (.e. 95 % confidence interval).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/conf_lvl_to_quantile_prob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile probabilities from confidence level — conf_lvl_to_quantile_prob","text":"list two numerical vectors, \"lower\" \"upper\".","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/conf_lvl_to_quantile_prob.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile probabilities from confidence level — conf_lvl_to_quantile_prob","text":"","code":"conf_lvl_to_quantile_prob(0.95) #> $lower #> [1] 0.025 #>  #> $upper #> [1] 0.975 #>"},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_prr.html","id":null,"dir":"Reference","previous_headings":"","what":"Count expected for Proportional Reporting Rate — count_expected_prr","title":"Count expected for Proportional Reporting Rate — count_expected_prr","text":"Internal function provide expected counts related PRR","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_prr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count expected for Proportional Reporting Rate — count_expected_prr","text":"","code":"count_expected_prr(count_dt)"},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_prr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count expected for Proportional Reporting Rate — count_expected_prr","text":"count_dt data table, output count_expected_rrr","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_prr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count expected for Proportional Reporting Rate — count_expected_prr","text":"data table added columns n_event_prr n_tot_prr expected_prr  @export","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_ror.html","id":null,"dir":"Reference","previous_headings":"","what":"Count expected for Reporting Odds Ratio — count_expected_ror","title":"Count expected for Reporting Odds Ratio — count_expected_ror","text":"Internal function provide expected counts related ROR","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_ror.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count expected for Reporting Odds Ratio — count_expected_ror","text":"","code":"count_expected_ror(count_dt)"},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_ror.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count expected for Reporting Odds Ratio — count_expected_ror","text":"count_dt data table, output count_expected_rrr","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_ror.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count expected for Reporting Odds Ratio — count_expected_ror","text":"data table added columns n_event_prr, n_tot_prr expected_prr OUTPUT_DESCRIPTION","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_ror.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Count expected for Reporting Odds Ratio — count_expected_ror","text":"DETAILS","code":""},{"path":[]},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_rrr.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Expected for Relative Reporting Rate — count_expected_rrr","title":"Count Expected for Relative Reporting Rate — count_expected_rrr","text":"Internal function provide expected counts related RRR","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_rrr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Expected for Relative Reporting Rate — count_expected_rrr","text":"","code":"count_expected_rrr(df, df_colnames, df_syms)"},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_rrr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Expected for Relative Reporting Rate — count_expected_rrr","text":"df See documentation add_expected_counts df_colnames See documentation da df_syms list built df_colnames conversion symbols.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_rrr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Expected for Relative Reporting Rate — count_expected_rrr","text":"data frame columns obs, n_drug, n_event, n_tot (RRR) expected","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/da.html","id":null,"dir":"Reference","previous_headings":"","what":"Disproportionality Analysis — da","title":"Disproportionality Analysis — da","text":"function da executes disproportionality analyses, .e. compares proportion reports specific adverse event drug, event proportion comparator based passed data frame. See vignette brief introduction disproportionality analysis. Furthermore, da supports three estimators: Information Component (IC), Proportional Reporting Rate (PRR) Reporting Odds Ratio (ROR).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/da.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Disproportionality Analysis — da","text":"","code":"da(   df = NULL,   df_colnames = list(report_id = \"report_id\", drug = \"drug\", event = \"event\", group_by =     NULL),   da_estimators = c(\"ic\", \"prr\", \"ror\"),   sort_by = \"ic\",   number_of_digits = 2,   rule_of_N = 3,   conf_lvl = 0.95,   excel_path = NULL )"},{"path":"https://oskargauffin.github.io/pvutils/reference/da.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Disproportionality Analysis — da","text":"df object possible convert data table, e.g. tibble data.frame, containing patient level reported drug-event-pairs. See header 'df object' details. df_colnames list column names use df. , point da 'report id'-column (report_id), 'drug name'-column (drug), 'adverse event'-column (event) optionally grouping column group_by calculate disproportionality across. See vignette details. da_estimators Character vector specifying disproportionality estimators use, case need implemented options. Defaults c(\"ic\", \"prr\", \"ror\"). sort_by output sorted descending order lower bound confidence/credibility interval passed da estimator. passed strings \"da_estimators\" accepted, default \"ic\". grouping variable passed, sorting made sample average across drug-event-combination (ignoring NAs). number_of_digits Round decimal columns specified precision, default two decimals. rule_of_N Numeric value. Sets estimates ROR PRR NA observed counts strictly less passed value rule_of_N. Default value 3, 5 sometimes used liberal alternative. Set NULL want apply rule. conf_lvl Confidence level confidence credibility intervals. Default 0.95 (.e. 95 % confidence interval). excel_path Intended users prefer work excel minimal work R. write output da excel file, provide path folder. instance, write current working directory, pass getwd().  excel file default named da.xlsx. control excel file name,  pass path ending desired filename suffixed .xlsx.  want export output excel file, pass NULL (default).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/da.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Disproportionality Analysis — da","text":"da returns data frame (invisibly) containing counts estimates related supported disproportionality estimators. row corresponds drug-event pair.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/da.html","id":"the-df-object","dir":"Reference","previous_headings":"","what":"The df object","title":"Disproportionality Analysis — da","text":"passed df (convertible ) data table least contain three  columns: report_id, drug event. data table contain one row  per reported drug-event-combination, .e. receiving single additional report  drug X event Y add one row table. single report  contained drug X event Y event Z, two rows added,  report_id drug rows. Column report_id must type  numeric character. Columns drug event must type character.  column group_by provided, can either numeric character.  can use df column names choosing, long  connect role name df_colnames-parameter.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/da.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Disproportionality Analysis — da","text":"","code":"### Run a disproportionality analysis  da_1 <-   tiny_dataset |>   da()  ### Run a disproportionality across subgroups list_of_colnames <-   list(     report_id = \"report_id\",     drug = \"drug\",     event = \"event\",     group_by = \"group\"   )   da_2 <-   tiny_dataset |>   da(df_colnames = list_of_colnames)  # If columns in your df have different names than the default ones, # you can specify the column names in the df_colnames parameter list:  renamed_df <-   tiny_dataset |>   dplyr::rename(ReportID = report_id)  list_of_colnames$report_id <- \"ReportID\"  da_3 <-   renamed_df |>   da(df_colnames = list_of_colnames)"},{"path":"https://oskargauffin.github.io/pvutils/reference/drug_event_df.html","id":null,"dir":"Reference","previous_headings":"","what":"A simulated ICSR database — drug_event_df","title":"A simulated ICSR database — drug_event_df","text":"drug_event_df simulated dataset, slightly larger \"tiny_dataset\" also contained package.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/drug_event_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A simulated ICSR database — drug_event_df","text":"","code":"drug_event_df"},{"path":"https://oskargauffin.github.io/pvutils/reference/drug_event_df.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A simulated ICSR database — drug_event_df","text":"`drug_event_df` data frame 3,971 rows 3 columns. total 1000 unique report_ids, .e. report_id can several drugs events. Number drugs per report_id sampled 1 + Pois(3), increasing probability drug letter closes Z. Every drug assigned event, decreasing probability event index number increases towards 1000. See DATASET.R file data-raw folder details. report_id patient report identifier drug One 26 fake drugs (Drug_A - Drug_Z) event Sampled events (Event_1 - Event_1000)","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/drug_event_df.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A simulated ICSR database — drug_event_df","text":"Simulated data.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/grouped_da.html","id":null,"dir":"Reference","previous_headings":"","what":"Disproportionality Analysis by Subgroups — grouped_da","title":"Disproportionality Analysis by Subgroups — grouped_da","text":"package internal wrapper executing da across subgroups","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/grouped_da.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Disproportionality Analysis by Subgroups — grouped_da","text":"","code":"grouped_da(   df = NULL,   df_colnames = NULL,   df_syms = NULL,   expected_count_estimators = NULL,   da_estimators = NULL,   sort_by = NULL,   conf_lvl = NULL,   rule_of_N = NULL,   number_of_digits = NULL )"},{"path":"https://oskargauffin.github.io/pvutils/reference/grouped_da.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Disproportionality Analysis by Subgroups — grouped_da","text":"df See da function df_colnames See da function df_syms list built df_colnames conversion symbols. expected_count_estimators See da function da_estimators See da function sort_by See da function conf_lvl See da function rule_of_N See da function number_of_digits See da function","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/grouped_da.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Disproportionality Analysis by Subgroups — grouped_da","text":"See da function","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/grouped_da.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Disproportionality Analysis by Subgroups — grouped_da","text":"See da documentation","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":null,"dir":"Reference","previous_headings":"","what":"Information component — ic","title":"Information component — ic","text":"Calculates information component (\"IC\") credibility interval, used disproportionality analysis.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Information component — ic","text":"","code":"ic(obs = NULL, exp = NULL, shrinkage = 0.5, conf_lvl = 0.95)"},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Information component — ic","text":"obs numeric vector observed counts, .e. number reports selected drug-event-combination. Note shrinkage (e.g. +0.5) added inside function included . exp numeric vector expected counts, .e. number reports expected given comparator background. Note shrinkage (e.g. +0.5) added inside function included . shrinkage non-negative numeric value, added observed expected count. Default 0.5. conf_lvl Confidence level confidence credibility intervals. Default 0.95 (.e. 95 % confidence interval).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Information component — ic","text":"tibble three columns (point estimate credibility bounds).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Information component — ic","text":"IC log2-transformed observed--expected ratio, based relative reporting rate (RRR) counts, modified addition \"shrinkage\" protect spurious associations. $$\\hat{IC} = log_{2}(\\frac{\\hat{O}+k}{\\hat{E}+k})$$ \\(\\hat{O}\\) = observed number reports, \\(k\\) shrinkage (typically +0.5), expected \\(\\hat{E}\\) (RRR, using entire database comparator background) estimated $$ \\hat{E} = \\frac{\\hat{N}_{drug} \\times \\hat{N}_{event}}{\\hat{N}_{TOT}}$$ \\(\\hat{N}_{drug}\\), \\(\\hat{N}_{event}\\) \\(\\hat{N}_{TOT}\\) number reports drug, event, whole database respectively. credibility interval created quantiles posterior gamma distribution shape (\\(\\hat{S}\\)) rate (\\(\\hat{R}\\)) parameters $$\\hat{S} = \\hat{O} + k$$ $$\\hat{R} = \\hat{E} + k$$ using stats::qgamma function. Parameter \\(k\\) shrinkage defined earlier. completeness, credibility interval gamma distributed \\(X\\) (.e. \\(X \\sim \\Gamma(\\hat{S}, \\hat{R})\\) \\(\\hat{S}\\) \\(\\hat{R}\\) shape rate parameters) associated quantile function \\(Q_X(p)\\) significance level \\(\\alpha\\) constructed $$[Q_X(\\alpha/2), Q_X(1-\\alpha/2)]$$","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":"further-details","dir":"Reference","previous_headings":"","what":"Further details","title":"Information component — ic","text":"bayesian point--view, credibility interval IC constructed poisson-gamma conjugacy. shrinkage constitutes prior observed expected 0.5. shrinkage +0.5 gamma-quantile based 95 % credibility interval lower bound 0 unless observed count exceeds 3. One benefit \\(log_{2}\\) provide log-scale convenient plotting multiple IC values side--side.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Information component — ic","text":"Norén GN, Hopstadius J, Bate (2011). “Shrinkage observed--expected ratios robust transparent large-scale pattern discovery.” Statistical Methods Medical Research, 22(1), 57--69. doi:10.1177/0962280211403604 , https://doi.org/10.1177/0962280211403604.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Information component — ic","text":"","code":"ic(obs = 20, exp = 10) #> # A tibble: 1 × 3 #>   ic2.5    ic ic97.5 #>   <dbl> <dbl>  <dbl> #> 1 0.264 0.965   1.53 # Note that obs and exp can be vectors (of equal length, no recycling allowed) ic(obs = c(20, 30), exp = c(10, 10)) #> # A tibble: 2 × 3 #>   ic2.5    ic ic97.5 #>   <dbl> <dbl>  <dbl> #> 1 0.264 0.965   1.53 #> 2 0.976 1.54    2.01"},{"path":"https://oskargauffin.github.io/pvutils/reference/print.da.html","id":null,"dir":"Reference","previous_headings":"","what":"print function for da objects — print.da","title":"print function for da objects — print.da","text":"print function da objects","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/print.da.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"print function for da objects — print.da","text":"","code":"# S3 method for da print(x, n = 10, ...)"},{"path":"https://oskargauffin.github.io/pvutils/reference/print.da.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"print function for da objects — print.da","text":"x S3 obj class \"da\", output pvda::da(). n Control number rows print. ... passing additional parameters extended classes.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/print.da.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"print function for da objects — print.da","text":"Nothing, prints tibble da_df da object.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/print.da.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"print function for da objects — print.da","text":"","code":"da_1 <- tiny_dataset |> da() print(da_1) #> Your console width is too narrow to support coloured printouts, reverting to non-coloured output. #> # A tibble: 107 × 23 #>    drug   event      obs exp_rrr ic2.5    ic ic97.5 exp_prr prr2.5   prr prr97.5 #>    <chr>  <chr>    <int>   <dbl> <dbl> <dbl>  <dbl>   <dbl>  <dbl> <dbl>   <dbl> #>  1 Drug_A Event_1      4    1.2  -0.34   1.4    2.5    0.63    1.6   6.4     26. #>  2 Drug_B Event_60     1    0.01 -2.2    1.6    3.2    0      NA    NA       NA  #>  3 Drug_G Event_80     1    0.01 -2.2    1.6    3.2    0      NA    NA       NA  #>  4 Drug_H Event_57     1    0.01 -2.2    1.6    3.2    0      NA    NA       NA  #>  5 Drug_C Event_52     1    0.02 -2.3    1.5    3.2    0      NA    NA       NA  #>  6 Drug_C Event_92     1    0.02 -2.3    1.5    3.2    0      NA    NA       NA  #>  7 Drug_D Event_20     1    0.02 -2.3    1.5    3.2    0      NA    NA       NA  #>  8 Drug_D Event_25     1    0.02 -2.3    1.5    3.2    0      NA    NA       NA  #>  9 Drug_I Event_37     1    0.02 -2.3    1.5    3.2    0      NA    NA       NA  #> 10 Drug_I Event_97     1    0.02 -2.3    1.5    3.2    0      NA    NA       NA  #> # ℹ 97 more rows #> # ℹ 12 more variables: exp_ror <dbl>, ror2.5 <dbl>, ror <dbl>, ror97.5 <dbl>, #> #   n_drug <int>, n_event <int>, n_tot <int>, n_event_prr <int>, #> #   n_tot_prr <int>, b <int>, c <int>, d <int> #> (Drug-event-combinations sorted according to lower bound of ic)  #> # Printing x[['da_df']]"},{"path":"https://oskargauffin.github.io/pvutils/reference/prr.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportional Reporting Rate — prr","title":"Proportional Reporting Rate — prr","text":"Calculates Proportional Reporting Rate (\"PRR\") confidence intervals, used disproportionality analysis.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/prr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportional Reporting Rate — prr","text":"","code":"prr(   obs = NULL,   n_drug = NULL,   n_event_prr = NULL,   n_tot_prr = NULL,   conf_lvl = 0.95 )"},{"path":"https://oskargauffin.github.io/pvutils/reference/prr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportional Reporting Rate — prr","text":"obs Number reports specific drug event (.e. observed count). n_drug Number reports drug interest. n_event_prr Number reports event background. n_tot_prr Number reports background. conf_lvl Confidence level confidence credibility intervals. Default 0.95 (.e. 95 % confidence interval).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/prr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportional Reporting Rate — prr","text":"tibble three columns (point estimate credibility bounds). Number rows equals length inputs obs, n_drug, n_event_prr n_tot_prr.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/prr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Proportional Reporting Rate — prr","text":"PRR proportion reports event set exposed cases, divided proportion reports event background comparator, include exposed. PRR estimated observed--expected ratio, based similar RRR IC, excludes exposure interest comparator. $$\\hat{PRR} = \\frac{\\hat{O}}{\\hat{E}}$$ \\(\\hat{O}\\) observed number reports, expected \\(\\hat{E}\\) estimated $$\\hat{E} = \\frac{\\hat{N}_{drug} \\times (\\hat{N}_{event} - \\hat{O})}{\\hat{N}_{TOT}-\\hat{N}_{drug}}$$ \\(\\hat{N}_{drug}\\), \\(\\hat{N}_{event}\\), \\(\\hat{O}\\) \\(\\hat{N}_{TOT}\\) number reports drug, event, drug event, whole database respectively. confidence interval derived Gravel (2009) using delta method: $$\\hat{s} = \\sqrt{ 1/\\hat{O} - 1/(\\hat{N}_{drug}) + 1/(\\hat{N}_{event} - \\hat{O}) - 1/(\\hat{N}_{TOT} - \\hat{N}_{drug})}$$ $$[\\hat{CI}_{\\alpha/2}, \\hat{CI}_{1-\\alpha/2}] = $$ $$[\\frac{\\hat{O}}{\\hat{E}} \\times \\exp(Q_{\\alpha/2} \\times \\hat{s}), \\frac{\\hat{O}}{\\hat{E}} \\times \\exp(Q_{1-\\alpha/2} \\times \\hat{s})]$$ \\(Q_{\\alpha}\\) denotes quantile function standard Normal distribution significance level \\(\\alpha\\). Note: historical reasons, another version standard deviation sometimes used last fraction square root added rather subtracted, negligible practical implications large databases. function uses version declared , .e. subtraction.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/prr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Proportional Reporting Rate — prr","text":"Montastruc J, Sommet , Bagheri H, Lapeyre-Mestre M (2011). “Benefits strengths disproportionality analysis identification adverse drug reactions pharmacovigilance database.” British Journal Clinical Pharmacology, 72(6), 905--908. doi:10.1111/j.1365-2125.2011.04037.x , https://doi.org/10.1111/j.1365-2125.2011.04037.x. Gravel C (2009). “Statistical Methods Signal Detection Pharmacovigilance.” https://repository.library.carleton.ca/downloads/jd472x08w.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/prr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Proportional Reporting Rate — prr","text":"","code":"prr(   obs = 5,   n_drug = 10,   n_event_prr = 20,   n_tot_prr = 10000 ) #> # A tibble: 1 × 3 #>   prr2.5   prr prr97.5 #>    <dbl> <dbl>   <dbl> #> 1   117.   250    534.   # Note that input parameters can be vectors (of equal length, no recycling) pvda::prr(   obs = c(5, 10),   n_drug = c(10, 20),   n_event_prr = c(15, 30),   n_tot_prr = c(10000, 10000) ) #> # A tibble: 2 × 3 #>   prr2.5   prr prr97.5 #>    <dbl> <dbl>   <dbl> #> 1  150.   333.    742. #> 2   94.7  167.    293."},{"path":"https://oskargauffin.github.io/pvutils/reference/ror.html","id":null,"dir":"Reference","previous_headings":"","what":"Reporting Odds Ratio — ror","title":"Reporting Odds Ratio — ror","text":"Calculates Reporting Odds Ratio (\"ROR\") confidence intervals, used disproportionality analysis.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ror.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reporting Odds Ratio — ror","text":"","code":"ror(a = NULL, b = NULL, c = NULL, d = NULL, conf_lvl = 0.95)"},{"path":"https://oskargauffin.github.io/pvutils/reference/ror.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reporting Odds Ratio — ror","text":"Number reports specific drug event (.e. observed count). b Number reports drug, without event c Number reports without drug, event d Number reports without drug, without event conf_lvl Confidence level confidence credibility intervals. Default 0.95 (.e. 95 % confidence interval).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ror.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reporting Odds Ratio — ror","text":"tibble three columns (point estimate credibility bounds). Number rows equals length inputs , b, c, d.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ror.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reporting Odds Ratio — ror","text":"ROR odds ratio calculated reporting counts. R Reporting ROR meant emphasize interpretation reporting, ROR calculated reporting database. Note: function vectorized, .e. , b, c d can vectors, see examples. reporting odds ratio simply odds ratio based adverse event reports. $$\\hat{ROR} = \\frac{/b}{c/d}$$ \\(\\) = observed count (.e. number reports exposure outcome), \\(b\\) = number reports drug without event, \\(c\\) = number reports without drug event \\(d\\) = number reports neither drug event. confidence interval ROR can derived delta method, standard deviation: $$\\hat{s} = \\sqrt{1/+ 1/b + 1/c + 1/d}$$ resulting confidence interval significance level \\(\\alpha\\) $$[\\hat{ROR} \\times exp(\\Phi_{\\alpha/2} \\times \\hat{s}), \\hat{ROR} \\times exp(\\Phi_{1-\\alpha/2} \\times \\hat{s})]$$","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ror.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Reporting Odds Ratio — ror","text":"Montastruc J, Sommet , Bagheri H, Lapeyre-Mestre M (2011). “Benefits strengths disproportionality analysis identification adverse drug reactions pharmacovigilance database.” British Journal Clinical Pharmacology, 72(6), 905--908. doi:10.1111/j.1365-2125.2011.04037.x , https://doi.org/10.1111/j.1365-2125.2011.04037.x.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ror.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reporting Odds Ratio — ror","text":"","code":"ror(   a = 5,   b = 10,   c = 20,   d = 10000 ) #> # A tibble: 1 × 3 #>   ror2.5   ror ror97.5 #>    <dbl> <dbl>   <dbl> #> 1   78.4   250    797.  # Note that a, b, c and d can be vectors (of equal length, no recycling) pvda::ror(   a = c(5, 10),   b = c(10, 20),   c = c(15, 30),   d = c(10000, 10000) ) #> # A tibble: 2 × 3 #>   ror2.5   ror ror97.5 #>    <dbl> <dbl>   <dbl> #> 1  102.   333.   1092. #> 2   72.0  167.    386."},{"path":"https://oskargauffin.github.io/pvutils/reference/round_and_sort_by_lower_da_limit.html","id":null,"dir":"Reference","previous_headings":"","what":"Sort a disproportionality analysis by the lower da conf. or cred. limit — round_and_sort_by_lower_da_limit","title":"Sort a disproportionality analysis by the lower da conf. or cred. limit — round_and_sort_by_lower_da_limit","text":"Sorts output mean lower limit passed da estimator","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/round_and_sort_by_lower_da_limit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sort a disproportionality analysis by the lower da conf. or cred. limit — round_and_sort_by_lower_da_limit","text":"","code":"round_and_sort_by_lower_da_limit(   df = NULL,   df_colnames = NULL,   df_syms = NULL,   conf_lvl = NULL,   sort_by = NULL,   da_estimators = NULL,   number_of_digits = 2 )"},{"path":"https://oskargauffin.github.io/pvutils/reference/round_and_sort_by_lower_da_limit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sort a disproportionality analysis by the lower da conf. or cred. limit — round_and_sort_by_lower_da_limit","text":"df See add_disproportionality df_colnames See add_disproportionality df_syms See add_disproportionality conf_lvl See add_disproportionality sort_by See add_disproportionality da_estimators See add_disproportionality number_of_digits Numeric value. Set number digits show output passing integer. Default value 2 digits. Set NULL avoid rounding.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/round_and_sort_by_lower_da_limit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sort a disproportionality analysis by the lower da conf. or cred. limit — round_and_sort_by_lower_da_limit","text":"df object, sorted.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/round_columns_with_many_decimals.html","id":null,"dir":"Reference","previous_headings":"","what":"Rounds columns in da_df with many decimals — round_columns_with_many_decimals","title":"Rounds columns in da_df with many decimals — round_columns_with_many_decimals","text":"Internal function containing mutate + across","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/round_columns_with_many_decimals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rounds columns in da_df with many decimals — round_columns_with_many_decimals","text":"","code":"round_columns_with_many_decimals(   da_df = NULL,   da_estimators = NULL,   number_of_digits = NULL )"},{"path":"https://oskargauffin.github.io/pvutils/reference/round_columns_with_many_decimals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rounds columns in da_df with many decimals — round_columns_with_many_decimals","text":"da_df See add_disproportionality da_estimators See add_disproportionality number_of_digits See add_disproportionality","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/round_columns_with_many_decimals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rounds columns in da_df with many decimals — round_columns_with_many_decimals","text":"df rounded columns","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/summary.da.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary function for disproportionality objects — summary.da","title":"Summary function for disproportionality objects — summary.da","text":"Provides summary counts SDRs shows top five DECs","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/summary.da.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary function for disproportionality objects — summary.da","text":"","code":"# S3 method for da summary(object, print = TRUE, ...)"},{"path":"https://oskargauffin.github.io/pvutils/reference/summary.da.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary function for disproportionality objects — summary.da","text":"object S3 obj class \"da\", output pvda::da(). print want print output console. Defaults TRUE. ... passing additional parameters extended classes.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/summary.da.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary function for disproportionality objects — summary.da","text":"Passes tibble SDR counts invisibly.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/tiny_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"A 110 reports big, simulated ICSR database — tiny_dataset","title":"A 110 reports big, simulated ICSR database — tiny_dataset","text":"dataframe tiny_dataset used demonstrate functionality package examples. larger drug_event_df-dataset can also used.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/tiny_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A 110 reports big, simulated ICSR database — tiny_dataset","text":"","code":"tiny_dataset"},{"path":"https://oskargauffin.github.io/pvutils/reference/tiny_dataset.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A 110 reports big, simulated ICSR database — tiny_dataset","text":"`tiny_dataset` data frame 110 rows 3 columns. total 110 unique report_ids. particular, Drug Event 1 observed count 4 exp_rrr = 1.1 report_id report identifier, 1-110. drug Drugs named Drug_A - Drug_Z. event Events named Event_1 - Event_97) group example, sex patient, .e. Male Female.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/tiny_dataset.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A 110 reports big, simulated ICSR database — tiny_dataset","text":"Simulated data.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/write_to_excel.html","id":null,"dir":"Reference","previous_headings":"","what":"Write to excel — write_to_excel","title":"Write to excel — write_to_excel","text":"Writes output disproportionality analysis excel file","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/write_to_excel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write to excel — write_to_excel","text":"","code":"write_to_excel(df, write_path = NULL)"},{"path":"https://oskargauffin.github.io/pvutils/reference/write_to_excel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write to excel — write_to_excel","text":"df data frame export. See `?da` details. write_path string giving file path","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/write_to_excel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Write to excel — write_to_excel","text":"Nothing.","code":""},{"path":"https://oskargauffin.github.io/pvutils/news/index.html","id":"pvda-001","dir":"Changelog","previous_headings":"","what":"pvda 0.0.1","title":"pvda 0.0.1","text":"CRAN release: 2024-01-17 first version package.","code":""},{"path":"https://oskargauffin.github.io/pvutils/news/index.html","id":"version-002","dir":"Changelog","previous_headings":"","what":"version 0.0.2","title":"version 0.0.2","text":"CRAN release: 2024-01-18 Fixing minor bug discovered CRAN checks.","code":""},{"path":"https://oskargauffin.github.io/pvutils/news/index.html","id":"version-003","dir":"Changelog","previous_headings":"","what":"version 0.0.3","title":"version 0.0.3","text":"Fixing bug related summary grouping variables. Removed export tag internal functions cleaner reference manual, added pkgdown site.","code":""}]
+[{"path":"https://oskargauffin.github.io/pvutils/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc. <http://fsf.org/> Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"https://oskargauffin.github.io/pvutils/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. 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Use with the GNU Affero General Public License","title":"GNU General Public License","text":"Notwithstanding provision License, permission link combine covered work work licensed version 3 GNU Affero General Public License single combined work, convey resulting work. terms License continue apply part covered work, special requirements GNU Affero General Public License, section 13, concerning interaction network apply combination .","code":""},{"path":"https://oskargauffin.github.io/pvutils/LICENSE.html","id":"id_14-revised-versions-of-this-license","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"14. Revised Versions of this License","title":"GNU General Public License","text":"Free Software Foundation may publish revised /new versions GNU General Public License time time. new versions similar spirit present version, may differ detail address new problems concerns. version given distinguishing version number. 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Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"https://oskargauffin.github.io/pvutils/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least “copyright” line pointer full notice found. Also add information contact electronic paper mail. program terminal interaction, make output short notice like starts interactive mode: hypothetical commands show w show c show appropriate parts General Public License. course, program’s commands might different; GUI interface, use “box”. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU GPL, see <http://www.gnu.org/licenses/>. GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read <http://www.gnu.org/philosophy/--lgpl.html>.","code":"<one line to give the program's name and a brief idea of what it does.> Copyright (C) <year>  <name of author>  This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.  This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.  You should have received a copy of the GNU General Public License along with this program.  If not, see <http://www.gnu.org/licenses/>. <program>  Copyright (C) <year>  <name of author> This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Disproportionality analysis","text":"life cycle medicinal product, knowledge safety profile continues accumulate post market introduction. Adverse event reports collected spontaneous reporting play instrumental part development. Adverse event report databases often cover wide range drugs events, beyond can feasibly monitored manual work. Disproportionality analysis thus often used highlight drug-event-combinations potential interest qualitative manual case--case assessment. pvda package provides three disproportionality estimators: proportional reporting rate (PRR), reporting odds ratio (ROR) information component (IC). takes report level data input, outputs estimates drug-event-combination, possibly stratified variable interest, e.g. age group /sex.","code":""},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"running-a-disproportionality-analysis","dir":"Articles","previous_headings":"","what":"Running a disproportionality analysis","title":"Disproportionality analysis","text":"workhorse package function da. da-function takes patient level data data frame input (drug_event_df) produces estimates summary counts demonstrated . drug_event_df simulated data set drugs named Drug_A - Drug_Z events Event_1 - Event_1000. details simulation documented data object (execute ?df_drug_event).","code":"da_1 <-    drug_event_df |>   da()"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"specifying-the-column-names","dir":"Articles","previous_headings":"Running a disproportionality analysis","what":"Specifying the column names","title":"Disproportionality analysis","text":"da-function examine passed data set column names contained passed parameter list “df_colnames”. default column names “report_id”, “drug” “event”. rename report-id column “drug_event_df” need specify new name call da, shown :","code":"da_1 <-   drug_event_df |>    dplyr::rename(reportID = report_id) |>    da(df_colnames = list(report_id = \"reportID\"))"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"summarizing-and-printing-results","dir":"Articles","previous_headings":"Running a disproportionality analysis","what":"Summarizing and printing results","title":"Disproportionality analysis","text":"‘da_1’ object can passed summary print function.   (Technical note: printing colors console easily reproduced within vignette , provide screenshots applicable. )","code":"summary(da_1) print(da_1, n=3)"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"passing-a-correctly-structured-input-data","dir":"Articles","previous_headings":"Running a disproportionality analysis","what":"Passing a correctly structured input data","title":"Disproportionality analysis","text":"input data needs report-level format. every line data frame corresponds drug-event pair specific report. drug_event_df-example data, first three rows drug_event_df report, report_id = 1. first row reports Drug_D adverse event named Event_5.next three rows another report, instance drug B reported two different events, event 15 event 33. can ignore group-column now.","code":"drug_event_df[1:6, ] ## # A tibble: 6 × 4 ##   report_id drug   event    group ##       <int> <chr>  <chr>    <dbl> ## 1         1 Drug_A Event_23     1 ## 2         1 Drug_D Event_6      1 ## 3         1 Drug_E Event_8      1 ## 4         1 Drug_I Event_9      1 ## 5         2 Drug_B Event_2      0 ## 6         2 Drug_G Event_13     0"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"extracting-the-results","dir":"Articles","previous_headings":"Running a disproportionality analysis","what":"Extracting the results","title":"Disproportionality analysis","text":"covenience, output object da list class “da” containing two objects, results (“da_df”) input parameters (“input_params”). point might want extract results input parameters list, demonstrate alternatives:","code":"names(da_1) ## [1] \"da_df\"        \"input_params\" # Different ways of extracting the results data frame # da_1$da_df # da_1[['da_df']] # da_1 |> purrr::pluck(\"da_df\")"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"how-to-run-a-subgroup-analysis","dir":"Articles","previous_headings":"Running a disproportionality analysis","what":"How to run a subgroup analysis?","title":"Disproportionality analysis","text":"da function builds comparator passed data, e.g. prr excluding drug interest comparator, sometimes referred background. implies subgroup analysis e.g. specific age groups can achieved creating new data frame subgroup interest, passing da. exhaustive grouping required, one can provide grouping variable group_by da. executes call da grouping return results single data frame. grouping instance age groups, sex, countries, regions combination thereof, needs passed single variable (character numeric). drug_event_df contains two groups, column named “group”. can pass “group” group_by parameter df_colnames . output note third column “group”, .e. column name passed. also note drug-event-pairs kept together, e.g. first second row contains drug-event-combination, “Drug_Z” “Event_1”. simplify comparisons within drug-event-pair across different groups, e.g. pattern seen across males females. ordering drug-events output made averaging selected disproportionality estimate (parameter sort_by), .e. top two rows highest average “ic2.5”.","code":"drug_event_df |>    head() ## # A tibble: 6 × 4 ##   report_id drug   event    group ##       <int> <chr>  <chr>    <dbl> ## 1         1 Drug_A Event_23     1 ## 2         1 Drug_D Event_6      1 ## 3         1 Drug_E Event_8      1 ## 4         1 Drug_I Event_9      1 ## 5         2 Drug_B Event_2      0 ## 6         2 Drug_G Event_13     0 da_grouped <-    drug_event_df |>    da(df_colnames = list(group_by=\"group\"))   print(da_grouped$da_df) ## # A tibble: 1,808 × 24 ##    drug   event   group   obs exp_rrr ic2.5    ic ic97.5 exp_prr prr2.5   prr ##    <chr>  <chr>   <dbl> <int>   <dbl> <dbl> <dbl>  <dbl>   <dbl>  <dbl> <dbl> ##  1 Drug_Z Event_1     0    45    26.5  0.3   0.75   1.14    23.4   1.54  1.92 ##  2 Drug_Z Event_1     1    43    29.1  0.09  0.56   0.96    26.5   1.28  1.63 ##  3 Drug_Z Event_2     0    33    24.6 -0.12  0.42   0.87    23.2   1.07  1.42 ##  4 Drug_Z Event_2     1    42    27.8  0.12  0.59   0.99    25.1   1.3   1.67 ##  5 Drug_V Event_2     0    34    26.0 -0.14  0.38   0.82    24.6   1.04  1.38 ##  6 Drug_V Event_2     1    46    31.0  0.12  0.56   0.95    27.8   1.3   1.66 ##  7 Drug_V Event_1     0    40    28.0  0.03  0.51   0.92    25.8   1.21  1.55 ##  8 Drug_V Event_1     1    42    32.4 -0.1   0.37   0.77    30.3   1.07  1.38 ##  9 Drug_U Event_2     0    42    31.5 -0.06  0.41   0.81    29.1   1.11  1.44 ## 10 Drug_U Event_2     1    37    26.8 -0.04  0.46   0.89    24.9   1.14  1.49 ## # ℹ 1,798 more rows ## # ℹ 13 more variables: prr97.5 <dbl>, exp_ror <dbl>, ror2.5 <dbl>, ror <dbl>, ## #   ror97.5 <dbl>, n_drug <int>, n_event <int>, n_tot <int>, n_event_prr <int>, ## #   n_tot_prr <int>, b <int>, c <int>, d <int>"},{"path":[]},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"how-are-drug-event-pairs-occurrings-several-times-in-one-report-counted","dir":"Articles","previous_headings":"Frequently Asked Questions","what":"How are drug-event pairs occurrings several times in one report counted?","title":"Disproportionality analysis","text":"drug-event pair occurs several times one report, contributes counts . , observed count five means five different reports containing drug-event-pair (least ). due single report containing drug-event pair five times due repeated events data quality issues. demonstrated , duplicate first row drug_event_df still arrive observed count 1.","code":"first_row <- drug_event_df[1,]  first_row |>    dplyr::bind_rows(first_row) |>    da() |>    purrr::pluck(\"da_df\") |>    dplyr::pull(obs) ## [1] 1"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"how-is-the-expected-calculated","dir":"Articles","previous_headings":"Frequently Asked Questions","what":"How is the expected calculated?","title":"Disproportionality analysis","text":"Detailed formulas PRR, RRR IC documented within function ( “?prr”, “?rrr”, “?ic”), overall one can note expected counts derived contingency table exposed drug, experience adverse event. sample used contingency table data frame passed da function. See also grouping parameter.","code":""},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"where-is-exp_ic-in-the-output-or-what-is-exp_prr","dir":"Articles","previous_headings":"Frequently Asked Questions","what":"Where is exp_ic in the output, or what is exp_prr?","title":"Disproportionality analysis","text":"common question regards output column names, ic preceded “exp_prr”. information component uses expected count Relative Reporting Rate (RRR), perhaps well known pharmacovigilande community.","code":"# Note the fourth column name: drug_event_df |>    da() |>    purrr::pluck(\"da_df\") |>    dplyr::select(drug:ic97.5) |>    print(n=1) ## # A tibble: 1,306 × 7 ##   drug   event     obs exp_rrr ic2.5    ic ic97.5 ##   <chr>  <chr>   <int>   <dbl> <dbl> <dbl>  <dbl> ## 1 Drug_Z Event_1    88    55.6  0.34  0.66   0.94 ## # ℹ 1,305 more rows"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"rule-of-n","dir":"Articles","previous_headings":"Frequently Asked Questions","what":"Rule of N","title":"Disproportionality analysis","text":"function da several input parameters. One rule_of_N, default set 3, sometimes referred “rule three”. sets ROR PRR-values NA observed count less specified N. completeness, note default shrinkage IC acts built ‘rule 3’, (.e. shrinkage +0.5 prevents lower bound exceed 0 default significance level 95%).","code":""},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"controlling-the-number-of-digits","dir":"Articles","previous_headings":"Frequently Asked Questions","what":"Controlling the number of digits","title":"Disproportionality analysis","text":"function da parameter number_of_digits controls rounding non-count values output, including expected counts, uncertainty bounds point estimates.","code":"drug_event_df |>   da(     rule_of_N = 3,     number_of_digits = 8   ) |> print(n = 3)"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"can-i-use-pvda-to-get-ic-prr-or-ror-when-i-already-have-the-observed-and-expected-counts","dir":"Articles","previous_headings":"Frequently Asked Questions","what":"Can I use pvda to get IC, PRR or ROR, when I already have the observed and expected counts?","title":"Disproportionality analysis","text":"Yes, can calculate point estimates confidence credibility intervals specific drug-event-combination already number observed reports, required counts database total count, number reports exposure number reports event. can helpful instance quality check results, e.g. papers software. details specific input parameters, consult documentation function.","code":"prr(obs = 10, n_drug = 1000, n_event_prr = 200, n_tot_prr = 10000) ## # A tibble: 1 × 3 ##   prr2.5   prr prr97.5 ##    <dbl> <dbl>   <dbl> ## 1  0.266   0.5   0.940 ror(a = 10, b = 20, c = 200, d = 10000) ## # A tibble: 1 × 3 ##   ror2.5   ror ror97.5 ##    <dbl> <dbl>   <dbl> ## 1   11.6    25    54.1 ic(obs = 10, exp = 5) ## # A tibble: 1 × 3 ##     ic2.5    ic ic97.5 ##     <dbl> <dbl>  <dbl> ## 1 -0.0973 0.933   1.69"},{"path":"https://oskargauffin.github.io/pvutils/articles/Disproportionality_analysis.html","id":"how-fast-is-pvda","dir":"Articles","previous_headings":"Frequently Asked Questions","what":"How fast is pvda?","title":"Disproportionality analysis","text":"data.table-package (“dtplyr”) used fast execution. test execution speed made using vaers R package, available gitlab. vaers package contains data VAERS years 1990 - 2018, resulting 4 146 778 rows processed da pvda. Execution time regular laptop less 10 seconds.","code":""},{"path":"https://oskargauffin.github.io/pvutils/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Oskar Gauffin. Author, maintainer.","code":""},{"path":"https://oskargauffin.github.io/pvutils/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Gauffin O (2024). pvda: Disproportionality Functions Pharmacovigilance. R package version 0.0.3, https://oskargauffin.github.io/pvda/.","code":"@Manual{,   title = {pvda: Disproportionality Functions for Pharmacovigilance},   author = {Oskar Gauffin},   year = {2024},   note = {R package version 0.0.3},   url = {https://oskargauffin.github.io/pvda/}, }"},{"path":"https://oskargauffin.github.io/pvutils/index.html","id":"pvda-","dir":"","previous_headings":"","what":"Disproportionality Functions for Pharmacovigilance","title":"Disproportionality Functions for Pharmacovigilance","text":"R package executing disproportionality analyses pharmacovigilance, using information component (IC), proportional reporting rate (PRR) reporting odds ratio (ROR).","code":""},{"path":"https://oskargauffin.github.io/pvutils/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Disproportionality Functions for Pharmacovigilance","text":"","code":"# Install stable version from CRAN  install.packages(\"pvda\")"},{"path":"https://oskargauffin.github.io/pvutils/index.html","id":"example-code","dir":"","previous_headings":"","what":"Example code","title":"Disproportionality Functions for Pharmacovigilance","text":"run disproportionality analysis, pass adverse event report-level data (, drug_event_df) function da : extract results data frame, access “da_df” list object:","code":"library(\"pvda\")  da1 <-  drug_event_df |>  da()  summary(da1) da_results <-  da1$da_df"},{"path":"https://oskargauffin.github.io/pvutils/reference/add_disproportionality.html","id":null,"dir":"Reference","previous_headings":"","what":"Add disproportionality estimates to data frame\nwith expected counts — add_disproportionality","title":"Add disproportionality estimates to data frame\nwith expected counts — add_disproportionality","text":"Add disproportionality estimates data frame expected counts","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/add_disproportionality.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add disproportionality estimates to data frame\nwith expected counts — add_disproportionality","text":"","code":"add_disproportionality(   df = NULL,   df_syms = NULL,   da_estimators = c(\"ic\", \"prr\", \"ror\"),   rule_of_N = 3,   conf_lvl = 0.95 )"},{"path":"https://oskargauffin.github.io/pvutils/reference/add_disproportionality.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add disproportionality estimates to data frame\nwith expected counts — add_disproportionality","text":"df Intended use output tibble add_expected_counts. df_syms list built df_colnames conversion symbols. da_estimators Character vector specifying disproportionality estimators use, case need implemented options. Defaults c(\"ic\", \"prr\", \"ror\"). rule_of_N Numeric value. Sets estimates ROR PRR NA observed counts strictly less passed value rule_of_N. Default value 3, 5 sometimes used liberal alternative. Set NULL want apply rule. conf_lvl Confidence level confidence credibility intervals. Default 0.95 (.e. 95 % confidence interval).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/add_disproportionality.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add disproportionality estimates to data frame\nwith expected counts — add_disproportionality","text":"passed data frame disproportionality point interval estimates.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/add_expected_counts.html","id":null,"dir":"Reference","previous_headings":"","what":"Produces expected counts — add_expected_counts","title":"Produces expected counts — add_expected_counts","text":"Produces various counts used disproportionality analysis.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/add_expected_counts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Produces expected counts — add_expected_counts","text":"","code":"add_expected_counts(   df = NULL,   df_colnames = NULL,   df_syms = NULL,   expected_count_estimators = c(\"rrr\", \"prr\", \"ror\") )"},{"path":"https://oskargauffin.github.io/pvutils/reference/add_expected_counts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Produces expected counts — add_expected_counts","text":"df object possible convert data table, e.g. tibble data.frame, containing patient level reported drug-event-pairs. See header 'df object' details. df_colnames list column names use df. , point da 'report id'-column (report_id), 'drug name'-column (drug), 'adverse event'-column (event) optionally grouping column group_by calculate disproportionality across. See vignette details. df_syms list built df_colnames conversion symbols. expected_count_estimators character vector containing desired expected count estimators. Defaults c(\"rrr\", \"prr\", \"ror\").","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/add_expected_counts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Produces expected counts — add_expected_counts","text":"tibble containing various counts.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/add_expected_counts.html","id":"the-df-object","dir":"Reference","previous_headings":"","what":"The df object","title":"Produces expected counts — add_expected_counts","text":"passed df (convertible ) data table least contain three  columns: report_id, drug event. data table contain one row  per reported drug-event-combination, .e. receiving single additional report  drug X event Y add one row table. single report  contained drug X event Y event Z, two rows added,  report_id drug rows. Column report_id must type  numeric character. Columns drug event must type character.  column group_by provided, can either numeric character.  can use df column names choosing, long  connect role name df_colnames-parameter.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/apply_rule_of_N.html","id":null,"dir":"Reference","previous_headings":"","what":"apply_rule_of_N — apply_rule_of_N","title":"apply_rule_of_N — apply_rule_of_N","text":"Internal function set disproportionality cells ROR PRR NA observed count < 3","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/apply_rule_of_N.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"apply_rule_of_N — apply_rule_of_N","text":"","code":"apply_rule_of_N(   da_df = NULL,   da_estimators = c(\"ic\", \"prr\", \"ror\"),   rule_of_N = NULL )"},{"path":"https://oskargauffin.github.io/pvutils/reference/apply_rule_of_N.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"apply_rule_of_N — apply_rule_of_N","text":"da_df See intermediate object da_df add_disproportionality da_estimators Default c(\"ic\", \"prr\", \"ror\"). rule_of_N length one integer 0 10.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/apply_rule_of_N.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"apply_rule_of_N — apply_rule_of_N","text":"input data frame (da_df) potentially cells set NA.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/apply_rule_of_N.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"apply_rule_of_N — apply_rule_of_N","text":"Sometimes, want protect spurious findings based small observed counts combined infinitesimal expected counts.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/build_colnames_da.html","id":null,"dir":"Reference","previous_headings":"","what":"An internal function creating colnames for da confidence/credibility bounds — build_colnames_da","title":"An internal function creating colnames for da confidence/credibility bounds — build_colnames_da","text":"Given output quantile_prob, da_name string, create column names PRR025, ROR025 IC025","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/build_colnames_da.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"An internal function creating colnames for da confidence/credibility bounds — build_colnames_da","text":"","code":"build_colnames_da(   quantile_prob = list(lower = 0.025, upper = 0.975),   da_name = NULL )"},{"path":"https://oskargauffin.github.io/pvutils/reference/build_colnames_da.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"An internal function creating colnames for da confidence/credibility bounds — build_colnames_da","text":"quantile_prob list two parameters, lower upper. Default: list(lower = 0.025, upper = 0.975) da_name string, \"ic\", \"prr\" \"ror\". Default: NULL","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/build_colnames_da.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"An internal function creating colnames for da confidence/credibility bounds — build_colnames_da","text":"list two symbols, inserted dtplyr-chain","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ic.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for Information Component (IC) — ci_for_ic","title":"Confidence intervals for Information Component (IC) — ci_for_ic","text":"Mainly used function ic. Produces quantiles posterior gamma distribution. Called twice ic create credibility intervals.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for Information Component (IC) — ci_for_ic","text":"","code":"ci_for_ic(obs, exp, conf_lvl_probs, shrinkage)"},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for Information Component (IC) — ci_for_ic","text":"obs numeric vector observed counts, .e. number reports selected drug-event-combination. Note shrinkage (e.g. +0.5) added inside function included . exp numeric vector expected counts, .e. number reports expected given comparator background. Note shrinkage (e.g. +0.5) added inside function included . conf_lvl_probs probabilities posterior, based passed confidence level (conf_lvl) ic. instance, sgn_lvl = .95 ic used, quantiles extracted sgn_lvl_probs 0.025 0.975. shrinkage non-negative numeric value, added observed expected count. Default 0.5.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for Information Component (IC) — ci_for_ic","text":"credibility interval specified input parameters.","code":""},{"path":[]},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_prr.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for Proportional Reporting Rate — ci_for_prr","title":"Confidence intervals for Proportional Reporting Rate — ci_for_prr","text":"Mainly use prr. Produces (symmetric, normality based) confidence bounds PRR, passed probability. Called twice prr create confidence intervals.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_prr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for Proportional Reporting Rate — ci_for_prr","text":"","code":"ci_for_prr(   obs = NULL,   n_drug = NULL,   n_event_prr = NULL,   n_tot_prr = NULL,   conf_lvl_probs = 0.95 )"},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_prr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for Proportional Reporting Rate — ci_for_prr","text":"obs Number reports specific drug event (.e. observed count). n_drug Number reports drug interest. n_event_prr Number reports event background. n_tot_prr Number reports background. conf_lvl_probs probabilities normal distribution, based passed confidence level (conf_lvl) prr. sgn_lvl = .95 prr, quantiles normal distribution extracted sgn_lvl_probs 0.025 0.975.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_prr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for Proportional Reporting Rate — ci_for_prr","text":"confidence interval specified input parameters.","code":""},{"path":[]},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ror.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for Reporting Odds Ratio — ci_for_ror","title":"Confidence intervals for Reporting Odds Ratio — ci_for_ror","text":"Mainly use ror. Produces (symmetric, normality based) confidence bounds ROR, passed probability. Called twice ror create confidence intervals.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ror.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for Reporting Odds Ratio — ci_for_ror","text":"","code":"ci_for_ror(a, b, c, d, conf_lvl_probs)"},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ror.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for Reporting Odds Ratio — ci_for_ror","text":"Number reports specific drug event (.e. observed count). b Number reports drug, without event c Number reports without drug, event d Number reports without drug, without event conf_lvl_probs probabilities normal distribution, based passed confidence level (conf_lvl) ror. sgn_lvl = .95 ror, quantiles normal distribution extracted sgn_lvl_probs 0.025 0.975.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ci_for_ror.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for Reporting Odds Ratio — ci_for_ror","text":"credibility interval specified input parameters.","code":""},{"path":[]},{"path":"https://oskargauffin.github.io/pvutils/reference/conf_lvl_to_quantile_prob.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile probabilities from confidence level — conf_lvl_to_quantile_prob","title":"Quantile probabilities from confidence level — conf_lvl_to_quantile_prob","text":"Calculates equi-tailed quantile probabilities confidence level","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/conf_lvl_to_quantile_prob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile probabilities from confidence level — conf_lvl_to_quantile_prob","text":"","code":"conf_lvl_to_quantile_prob(conf_lvl = 0.95)"},{"path":"https://oskargauffin.github.io/pvutils/reference/conf_lvl_to_quantile_prob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile probabilities from confidence level — conf_lvl_to_quantile_prob","text":"conf_lvl Confidence level confidence credibility intervals. Default 0.95 (.e. 95 % confidence interval).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/conf_lvl_to_quantile_prob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile probabilities from confidence level — conf_lvl_to_quantile_prob","text":"list two numerical vectors, \"lower\" \"upper\".","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/conf_lvl_to_quantile_prob.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile probabilities from confidence level — conf_lvl_to_quantile_prob","text":"","code":"conf_lvl_to_quantile_prob(0.95) #> $lower #> [1] 0.025 #>  #> $upper #> [1] 0.975 #>"},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_prr.html","id":null,"dir":"Reference","previous_headings":"","what":"Count expected for Proportional Reporting Rate — count_expected_prr","title":"Count expected for Proportional Reporting Rate — count_expected_prr","text":"Internal function provide expected counts related PRR","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_prr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count expected for Proportional Reporting Rate — count_expected_prr","text":"","code":"count_expected_prr(count_dt)"},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_prr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count expected for Proportional Reporting Rate — count_expected_prr","text":"count_dt data table, output count_expected_rrr","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_prr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count expected for Proportional Reporting Rate — count_expected_prr","text":"data table added columns n_event_prr n_tot_prr expected_prr  @export","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_ror.html","id":null,"dir":"Reference","previous_headings":"","what":"Count expected for Reporting Odds Ratio — count_expected_ror","title":"Count expected for Reporting Odds Ratio — count_expected_ror","text":"Internal function provide expected counts related ROR","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_ror.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count expected for Reporting Odds Ratio — count_expected_ror","text":"","code":"count_expected_ror(count_dt)"},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_ror.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count expected for Reporting Odds Ratio — count_expected_ror","text":"count_dt data table, output count_expected_rrr","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_ror.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count expected for Reporting Odds Ratio — count_expected_ror","text":"data table added columns n_event_prr, n_tot_prr expected_prr OUTPUT_DESCRIPTION","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_ror.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Count expected for Reporting Odds Ratio — count_expected_ror","text":"DETAILS","code":""},{"path":[]},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_rrr.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Expected for Relative Reporting Rate — count_expected_rrr","title":"Count Expected for Relative Reporting Rate — count_expected_rrr","text":"Internal function provide expected counts related RRR","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_rrr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Expected for Relative Reporting Rate — count_expected_rrr","text":"","code":"count_expected_rrr(df, df_colnames, df_syms)"},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_rrr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Expected for Relative Reporting Rate — count_expected_rrr","text":"df See documentation add_expected_counts df_colnames See documentation da df_syms list built df_colnames conversion symbols.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/count_expected_rrr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Expected for Relative Reporting Rate — count_expected_rrr","text":"data frame columns obs, n_drug, n_event, n_tot (RRR) expected","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/da.html","id":null,"dir":"Reference","previous_headings":"","what":"Disproportionality Analysis — da","title":"Disproportionality Analysis — da","text":"function da executes disproportionality analyses, .e. compares proportion reports specific adverse event drug, event proportion comparator based passed data frame. See vignette brief introduction disproportionality analysis. Furthermore, da supports three estimators: Information Component (IC), Proportional Reporting Rate (PRR) Reporting Odds Ratio (ROR).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/da.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Disproportionality Analysis — da","text":"","code":"da(   df = NULL,   df_colnames = list(report_id = \"report_id\", drug = \"drug\", event = \"event\", group_by =     NULL),   da_estimators = c(\"ic\", \"prr\", \"ror\"),   sort_by = \"ic\",   number_of_digits = 2,   rule_of_N = 3,   conf_lvl = 0.95,   excel_path = NULL )"},{"path":"https://oskargauffin.github.io/pvutils/reference/da.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Disproportionality Analysis — da","text":"df object possible convert data table, e.g. tibble data.frame, containing patient level reported drug-event-pairs. See header 'df object' details. df_colnames list column names use df. , point da 'report id'-column (report_id), 'drug name'-column (drug), 'adverse event'-column (event) optionally grouping column group_by calculate disproportionality across. See vignette details. da_estimators Character vector specifying disproportionality estimators use, case need implemented options. Defaults c(\"ic\", \"prr\", \"ror\"). sort_by output sorted descending order lower bound confidence/credibility interval passed da estimator. passed strings \"da_estimators\" accepted, default \"ic\". grouping variable passed, sorting made sample average across drug-event-combination (ignoring NAs). number_of_digits Round decimal columns specified precision, default two decimals. rule_of_N Numeric value. Sets estimates ROR PRR NA observed counts strictly less passed value rule_of_N. Default value 3, 5 sometimes used liberal alternative. Set NULL want apply rule. conf_lvl Confidence level confidence credibility intervals. Default 0.95 (.e. 95 % confidence interval). excel_path Intended users prefer work excel minimal work R. write output da excel file, provide path folder. instance, write current working directory, pass getwd().  excel file default named da.xlsx. control excel file name,  pass path ending desired filename suffixed .xlsx.  want export output excel file, pass NULL (default).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/da.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Disproportionality Analysis — da","text":"da returns data frame (invisibly) containing counts estimates related supported disproportionality estimators. row corresponds drug-event pair.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/da.html","id":"the-df-object","dir":"Reference","previous_headings":"","what":"The df object","title":"Disproportionality Analysis — da","text":"passed df (convertible ) data table least contain three  columns: report_id, drug event. data table contain one row  per reported drug-event-combination, .e. receiving single additional report  drug X event Y add one row table. single report  contained drug X event Y event Z, two rows added,  report_id drug rows. Column report_id must type  numeric character. Columns drug event must type character.  column group_by provided, can either numeric character.  can use df column names choosing, long  connect role name df_colnames-parameter.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/da.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Disproportionality Analysis — da","text":"","code":"### Run a disproportionality analysis  da_1 <-   tiny_dataset |>   da()  ### Run a disproportionality across subgroups list_of_colnames <-   list(     report_id = \"report_id\",     drug = \"drug\",     event = \"event\",     group_by = \"group\"   )   da_2 <-   tiny_dataset |>   da(df_colnames = list_of_colnames)  # If columns in your df have different names than the default ones, # you can specify the column names in the df_colnames parameter list:  renamed_df <-   tiny_dataset |>   dplyr::rename(ReportID = report_id)  list_of_colnames$report_id <- \"ReportID\"  da_3 <-   renamed_df |>   da(df_colnames = list_of_colnames)"},{"path":"https://oskargauffin.github.io/pvutils/reference/drug_event_df.html","id":null,"dir":"Reference","previous_headings":"","what":"A simulated ICSR database — drug_event_df","title":"A simulated ICSR database — drug_event_df","text":"drug_event_df simulated dataset, slightly larger \"tiny_dataset\" also contained package.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/drug_event_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A simulated ICSR database — drug_event_df","text":"","code":"drug_event_df"},{"path":"https://oskargauffin.github.io/pvutils/reference/drug_event_df.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A simulated ICSR database — drug_event_df","text":"`drug_event_df` data frame 3,971 rows 3 columns. total 1000 unique report_ids, .e. report_id can several drugs events. Number drugs per report_id sampled 1 + Pois(3), increasing probability drug letter closes Z. Every drug assigned event, decreasing probability event index number increases towards 1000. See DATASET.R file data-raw folder details. report_id patient report identifier drug One 26 fake drugs (Drug_A - Drug_Z) event Sampled events (Event_1 - Event_1000)","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/drug_event_df.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A simulated ICSR database — drug_event_df","text":"Simulated data.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/grouped_da.html","id":null,"dir":"Reference","previous_headings":"","what":"Disproportionality Analysis by Subgroups — grouped_da","title":"Disproportionality Analysis by Subgroups — grouped_da","text":"package internal wrapper executing da across subgroups","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/grouped_da.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Disproportionality Analysis by Subgroups — grouped_da","text":"","code":"grouped_da(   df = NULL,   df_colnames = NULL,   df_syms = NULL,   expected_count_estimators = NULL,   da_estimators = NULL,   sort_by = NULL,   conf_lvl = NULL,   rule_of_N = NULL,   number_of_digits = NULL )"},{"path":"https://oskargauffin.github.io/pvutils/reference/grouped_da.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Disproportionality Analysis by Subgroups — grouped_da","text":"df See da function df_colnames See da function df_syms list built df_colnames conversion symbols. expected_count_estimators See da function da_estimators See da function sort_by See da function conf_lvl See da function rule_of_N See da function number_of_digits See da function","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/grouped_da.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Disproportionality Analysis by Subgroups — grouped_da","text":"See da function","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/grouped_da.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Disproportionality Analysis by Subgroups — grouped_da","text":"See da documentation","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":null,"dir":"Reference","previous_headings":"","what":"Information component — ic","title":"Information component — ic","text":"Calculates information component (\"IC\") credibility interval, used disproportionality analysis.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Information component — ic","text":"","code":"ic(obs = NULL, exp = NULL, shrinkage = 0.5, conf_lvl = 0.95)"},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Information component — ic","text":"obs numeric vector observed counts, .e. number reports selected drug-event-combination. Note shrinkage (e.g. +0.5) added inside function included . exp numeric vector expected counts, .e. number reports expected given comparator background. Note shrinkage (e.g. +0.5) added inside function included . shrinkage non-negative numeric value, added observed expected count. Default 0.5. conf_lvl Confidence level confidence credibility intervals. Default 0.95 (.e. 95 % confidence interval).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Information component — ic","text":"tibble three columns (point estimate credibility bounds).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Information component — ic","text":"IC log2-transformed observed--expected ratio, based relative reporting rate (RRR) counts, modified addition \"shrinkage\" protect spurious associations. $$\\hat{IC} = log_{2}(\\frac{\\hat{O}+k}{\\hat{E}+k})$$ \\(\\hat{O}\\) = observed number reports, \\(k\\) shrinkage (typically +0.5), expected \\(\\hat{E}\\) (RRR, using entire database comparator background) estimated $$ \\hat{E} = \\frac{\\hat{N}_{drug} \\times \\hat{N}_{event}}{\\hat{N}_{TOT}}$$ \\(\\hat{N}_{drug}\\), \\(\\hat{N}_{event}\\) \\(\\hat{N}_{TOT}\\) number reports drug, event, whole database respectively. credibility interval created quantiles posterior gamma distribution shape (\\(\\hat{S}\\)) rate (\\(\\hat{R}\\)) parameters $$\\hat{S} = \\hat{O} + k$$ $$\\hat{R} = \\hat{E} + k$$ using stats::qgamma function. Parameter \\(k\\) shrinkage defined earlier. completeness, credibility interval gamma distributed \\(X\\) (.e. \\(X \\sim \\Gamma(\\hat{S}, \\hat{R})\\) \\(\\hat{S}\\) \\(\\hat{R}\\) shape rate parameters) associated quantile function \\(Q_X(p)\\) significance level \\(\\alpha\\) constructed $$[Q_X(\\alpha/2), Q_X(1-\\alpha/2)]$$","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":"further-details","dir":"Reference","previous_headings":"","what":"Further details","title":"Information component — ic","text":"bayesian point--view, credibility interval IC constructed poisson-gamma conjugacy. shrinkage constitutes prior observed expected 0.5. shrinkage +0.5 gamma-quantile based 95 % credibility interval lower bound 0 unless observed count exceeds 3. One benefit \\(log_{2}\\) provide log-scale convenient plotting multiple IC values side--side.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Information component — ic","text":"Norén GN, Hopstadius J, Bate (2011). “Shrinkage observed--expected ratios robust transparent large-scale pattern discovery.” Statistical Methods Medical Research, 22(1), 57--69. doi:10.1177/0962280211403604 , https://doi.org/10.1177/0962280211403604.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Information component — ic","text":"","code":"ic(obs = 20, exp = 10) #> # A tibble: 1 × 3 #>   ic2.5    ic ic97.5 #>   <dbl> <dbl>  <dbl> #> 1 0.264 0.965   1.53 # Note that obs and exp can be vectors (of equal length, no recycling allowed) ic(obs = c(20, 30), exp = c(10, 10)) #> # A tibble: 2 × 3 #>   ic2.5    ic ic97.5 #>   <dbl> <dbl>  <dbl> #> 1 0.264 0.965   1.53 #> 2 0.976 1.54    2.01"},{"path":"https://oskargauffin.github.io/pvutils/reference/print.da.html","id":null,"dir":"Reference","previous_headings":"","what":"print function for da objects — print.da","title":"print function for da objects — print.da","text":"print function da objects","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/print.da.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"print function for da objects — print.da","text":"","code":"# S3 method for da print(x, n = 10, ...)"},{"path":"https://oskargauffin.github.io/pvutils/reference/print.da.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"print function for da objects — print.da","text":"x S3 obj class \"da\", output pvda::da(). n Control number rows print. ... passing additional parameters extended classes.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/print.da.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"print function for da objects — print.da","text":"Nothing, prints tibble da_df da object.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/print.da.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"print function for da objects — print.da","text":"","code":"da_1 <- tiny_dataset |> da() print(da_1) #> Your console width is too narrow to support coloured printouts, reverting to non-coloured output. #> # A tibble: 107 × 23 #>    drug   event      obs exp_rrr ic2.5    ic ic97.5 exp_prr prr2.5   prr prr97.5 #>    <chr>  <chr>    <int>   <dbl> <dbl> <dbl>  <dbl>   <dbl>  <dbl> <dbl>   <dbl> #>  1 Drug_A Event_1      4    1.2  -0.34   1.4    2.5    0.63    1.6   6.4     26. #>  2 Drug_B Event_60     1    0.01 -2.2    1.6    3.2    0      NA    NA       NA  #>  3 Drug_G Event_80     1    0.01 -2.2    1.6    3.2    0      NA    NA       NA  #>  4 Drug_H Event_57     1    0.01 -2.2    1.6    3.2    0      NA    NA       NA  #>  5 Drug_C Event_52     1    0.02 -2.3    1.5    3.2    0      NA    NA       NA  #>  6 Drug_C Event_92     1    0.02 -2.3    1.5    3.2    0      NA    NA       NA  #>  7 Drug_D Event_20     1    0.02 -2.3    1.5    3.2    0      NA    NA       NA  #>  8 Drug_D Event_25     1    0.02 -2.3    1.5    3.2    0      NA    NA       NA  #>  9 Drug_I Event_37     1    0.02 -2.3    1.5    3.2    0      NA    NA       NA  #> 10 Drug_I Event_97     1    0.02 -2.3    1.5    3.2    0      NA    NA       NA  #> # ℹ 97 more rows #> # ℹ 12 more variables: exp_ror <dbl>, ror2.5 <dbl>, ror <dbl>, ror97.5 <dbl>, #> #   n_drug <int>, n_event <int>, n_tot <int>, n_event_prr <int>, #> #   n_tot_prr <int>, b <int>, c <int>, d <int> #> (Drug-event-combinations sorted according to lower bound of ic)  #> # Printing x[['da_df']]"},{"path":"https://oskargauffin.github.io/pvutils/reference/prr.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportional Reporting Rate — prr","title":"Proportional Reporting Rate — prr","text":"Calculates Proportional Reporting Rate (\"PRR\") confidence intervals, used disproportionality analysis.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/prr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportional Reporting Rate — prr","text":"","code":"prr(   obs = NULL,   n_drug = NULL,   n_event_prr = NULL,   n_tot_prr = NULL,   conf_lvl = 0.95 )"},{"path":"https://oskargauffin.github.io/pvutils/reference/prr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportional Reporting Rate — prr","text":"obs Number reports specific drug event (.e. observed count). n_drug Number reports drug interest. n_event_prr Number reports event background. n_tot_prr Number reports background. conf_lvl Confidence level confidence credibility intervals. Default 0.95 (.e. 95 % confidence interval).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/prr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportional Reporting Rate — prr","text":"tibble three columns (point estimate credibility bounds). Number rows equals length inputs obs, n_drug, n_event_prr n_tot_prr.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/prr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Proportional Reporting Rate — prr","text":"PRR proportion reports event set exposed cases, divided proportion reports event background comparator, include exposed. PRR estimated observed--expected ratio, based similar RRR IC, excludes exposure interest comparator. $$\\hat{PRR} = \\frac{\\hat{O}}{\\hat{E}}$$ \\(\\hat{O}\\) observed number reports, expected \\(\\hat{E}\\) estimated $$\\hat{E} = \\frac{\\hat{N}_{drug} \\times (\\hat{N}_{event} - \\hat{O})}{\\hat{N}_{TOT}-\\hat{N}_{drug}}$$ \\(\\hat{N}_{drug}\\), \\(\\hat{N}_{event}\\), \\(\\hat{O}\\) \\(\\hat{N}_{TOT}\\) number reports drug, event, drug event, whole database respectively. confidence interval derived Gravel (2009) using delta method: $$\\hat{s} = \\sqrt{ 1/\\hat{O} - 1/(\\hat{N}_{drug}) + 1/(\\hat{N}_{event} - \\hat{O}) - 1/(\\hat{N}_{TOT} - \\hat{N}_{drug})}$$ $$[\\hat{CI}_{\\alpha/2}, \\hat{CI}_{1-\\alpha/2}] = $$ $$[\\frac{\\hat{O}}{\\hat{E}} \\times \\exp(Q_{\\alpha/2} \\times \\hat{s}), \\frac{\\hat{O}}{\\hat{E}} \\times \\exp(Q_{1-\\alpha/2} \\times \\hat{s})]$$ \\(Q_{\\alpha}\\) denotes quantile function standard Normal distribution significance level \\(\\alpha\\). Note: historical reasons, another version standard deviation sometimes used last fraction square root added rather subtracted, negligible practical implications large databases. function uses version declared , .e. subtraction.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/prr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Proportional Reporting Rate — prr","text":"Montastruc J, Sommet , Bagheri H, Lapeyre-Mestre M (2011). “Benefits strengths disproportionality analysis identification adverse drug reactions pharmacovigilance database.” British Journal Clinical Pharmacology, 72(6), 905--908. doi:10.1111/j.1365-2125.2011.04037.x , https://doi.org/10.1111/j.1365-2125.2011.04037.x. Gravel C (2009). “Statistical Methods Signal Detection Pharmacovigilance.” https://repository.library.carleton.ca/downloads/jd472x08w.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/prr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Proportional Reporting Rate — prr","text":"","code":"prr(   obs = 5,   n_drug = 10,   n_event_prr = 20,   n_tot_prr = 10000 ) #> # A tibble: 1 × 3 #>   prr2.5   prr prr97.5 #>    <dbl> <dbl>   <dbl> #> 1   117.   250    534.   # Note that input parameters can be vectors (of equal length, no recycling) pvda::prr(   obs = c(5, 10),   n_drug = c(10, 20),   n_event_prr = c(15, 30),   n_tot_prr = c(10000, 10000) ) #> # A tibble: 2 × 3 #>   prr2.5   prr prr97.5 #>    <dbl> <dbl>   <dbl> #> 1  150.   333.    742. #> 2   94.7  167.    293."},{"path":"https://oskargauffin.github.io/pvutils/reference/ror.html","id":null,"dir":"Reference","previous_headings":"","what":"Reporting Odds Ratio — ror","title":"Reporting Odds Ratio — ror","text":"Calculates Reporting Odds Ratio (\"ROR\") confidence intervals, used disproportionality analysis.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ror.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reporting Odds Ratio — ror","text":"","code":"ror(a = NULL, b = NULL, c = NULL, d = NULL, conf_lvl = 0.95)"},{"path":"https://oskargauffin.github.io/pvutils/reference/ror.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reporting Odds Ratio — ror","text":"Number reports specific drug event (.e. observed count). b Number reports drug, without event c Number reports without drug, event d Number reports without drug, without event conf_lvl Confidence level confidence credibility intervals. Default 0.95 (.e. 95 % confidence interval).","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ror.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reporting Odds Ratio — ror","text":"tibble three columns (point estimate credibility bounds). Number rows equals length inputs , b, c, d.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ror.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reporting Odds Ratio — ror","text":"ROR odds ratio calculated reporting counts. R Reporting ROR meant emphasize interpretation reporting, ROR calculated reporting database. Note: function vectorized, .e. , b, c d can vectors, see examples. reporting odds ratio simply odds ratio based adverse event reports. $$\\hat{ROR} = \\frac{/b}{c/d}$$ \\(\\) = observed count (.e. number reports exposure outcome), \\(b\\) = number reports drug without event, \\(c\\) = number reports without drug event \\(d\\) = number reports neither drug event. confidence interval ROR can derived delta method, standard deviation: $$\\hat{s} = \\sqrt{1/+ 1/b + 1/c + 1/d}$$ resulting confidence interval significance level \\(\\alpha\\) $$[\\hat{ROR} \\times exp(\\Phi_{\\alpha/2} \\times \\hat{s}), \\hat{ROR} \\times exp(\\Phi_{1-\\alpha/2} \\times \\hat{s})]$$","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ror.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Reporting Odds Ratio — ror","text":"Montastruc J, Sommet , Bagheri H, Lapeyre-Mestre M (2011). “Benefits strengths disproportionality analysis identification adverse drug reactions pharmacovigilance database.” British Journal Clinical Pharmacology, 72(6), 905--908. doi:10.1111/j.1365-2125.2011.04037.x , https://doi.org/10.1111/j.1365-2125.2011.04037.x.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/ror.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reporting Odds Ratio — ror","text":"","code":"ror(   a = 5,   b = 10,   c = 20,   d = 10000 ) #> # A tibble: 1 × 3 #>   ror2.5   ror ror97.5 #>    <dbl> <dbl>   <dbl> #> 1   78.4   250    797.  # Note that a, b, c and d can be vectors (of equal length, no recycling) pvda::ror(   a = c(5, 10),   b = c(10, 20),   c = c(15, 30),   d = c(10000, 10000) ) #> # A tibble: 2 × 3 #>   ror2.5   ror ror97.5 #>    <dbl> <dbl>   <dbl> #> 1  102.   333.   1092. #> 2   72.0  167.    386."},{"path":"https://oskargauffin.github.io/pvutils/reference/round_and_sort_by_lower_da_limit.html","id":null,"dir":"Reference","previous_headings":"","what":"Sort a disproportionality analysis by the lower da conf. or cred. limit — round_and_sort_by_lower_da_limit","title":"Sort a disproportionality analysis by the lower da conf. or cred. limit — round_and_sort_by_lower_da_limit","text":"Sorts output mean lower limit passed da estimator","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/round_and_sort_by_lower_da_limit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sort a disproportionality analysis by the lower da conf. or cred. limit — round_and_sort_by_lower_da_limit","text":"","code":"round_and_sort_by_lower_da_limit(   df = NULL,   df_colnames = NULL,   df_syms = NULL,   conf_lvl = NULL,   sort_by = NULL,   da_estimators = NULL,   number_of_digits = 2 )"},{"path":"https://oskargauffin.github.io/pvutils/reference/round_and_sort_by_lower_da_limit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sort a disproportionality analysis by the lower da conf. or cred. limit — round_and_sort_by_lower_da_limit","text":"df See add_disproportionality df_colnames See add_disproportionality df_syms See add_disproportionality conf_lvl See add_disproportionality sort_by See add_disproportionality da_estimators See add_disproportionality number_of_digits Numeric value. Set number digits show output passing integer. Default value 2 digits. Set NULL avoid rounding.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/round_and_sort_by_lower_da_limit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sort a disproportionality analysis by the lower da conf. or cred. limit — round_and_sort_by_lower_da_limit","text":"df object, sorted.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/round_columns_with_many_decimals.html","id":null,"dir":"Reference","previous_headings":"","what":"Rounds columns in da_df with many decimals — round_columns_with_many_decimals","title":"Rounds columns in da_df with many decimals — round_columns_with_many_decimals","text":"Internal function containing mutate + across","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/round_columns_with_many_decimals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rounds columns in da_df with many decimals — round_columns_with_many_decimals","text":"","code":"round_columns_with_many_decimals(   da_df = NULL,   da_estimators = NULL,   number_of_digits = NULL )"},{"path":"https://oskargauffin.github.io/pvutils/reference/round_columns_with_many_decimals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rounds columns in da_df with many decimals — round_columns_with_many_decimals","text":"da_df See add_disproportionality da_estimators See add_disproportionality number_of_digits See add_disproportionality","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/round_columns_with_many_decimals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rounds columns in da_df with many decimals — round_columns_with_many_decimals","text":"df rounded columns","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/summary.da.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary function for disproportionality objects — summary.da","title":"Summary function for disproportionality objects — summary.da","text":"Provides summary counts SDRs shows top five DECs","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/summary.da.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary function for disproportionality objects — summary.da","text":"","code":"# S3 method for da summary(object, print = TRUE, ...)"},{"path":"https://oskargauffin.github.io/pvutils/reference/summary.da.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary function for disproportionality objects — summary.da","text":"object S3 obj class \"da\", output pvda::da(). print want print output console. Defaults TRUE. ... passing additional parameters extended classes.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/summary.da.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary function for disproportionality objects — summary.da","text":"Passes tibble SDR counts invisibly.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/tiny_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"A 110 reports big, simulated ICSR database — tiny_dataset","title":"A 110 reports big, simulated ICSR database — tiny_dataset","text":"dataframe tiny_dataset used demonstrate functionality package examples. larger drug_event_df-dataset can also used.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/tiny_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A 110 reports big, simulated ICSR database — tiny_dataset","text":"","code":"tiny_dataset"},{"path":"https://oskargauffin.github.io/pvutils/reference/tiny_dataset.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A 110 reports big, simulated ICSR database — tiny_dataset","text":"`tiny_dataset` data frame 110 rows 3 columns. total 110 unique report_ids. particular, Drug Event 1 observed count 4 exp_rrr = 1.1 report_id report identifier, 1-110. drug Drugs named Drug_A - Drug_Z. event Events named Event_1 - Event_97) group example, sex patient, .e. Male Female.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/tiny_dataset.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A 110 reports big, simulated ICSR database — tiny_dataset","text":"Simulated data.","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/write_to_excel.html","id":null,"dir":"Reference","previous_headings":"","what":"Write to excel — write_to_excel","title":"Write to excel — write_to_excel","text":"Writes output disproportionality analysis excel file","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/write_to_excel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write to excel — write_to_excel","text":"","code":"write_to_excel(df, write_path = NULL)"},{"path":"https://oskargauffin.github.io/pvutils/reference/write_to_excel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write to excel — write_to_excel","text":"df data frame export. See `?da` details. write_path string giving file path","code":""},{"path":"https://oskargauffin.github.io/pvutils/reference/write_to_excel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Write to excel — write_to_excel","text":"Nothing.","code":""},{"path":"https://oskargauffin.github.io/pvutils/news/index.html","id":"pvda-001","dir":"Changelog","previous_headings":"","what":"pvda 0.0.1","title":"pvda 0.0.1","text":"CRAN release: 2024-01-17 first version package.","code":""},{"path":"https://oskargauffin.github.io/pvutils/news/index.html","id":"version-002","dir":"Changelog","previous_headings":"","what":"version 0.0.2","title":"version 0.0.2","text":"CRAN release: 2024-01-18 Fixing minor bug discovered CRAN checks.","code":""},{"path":"https://oskargauffin.github.io/pvutils/news/index.html","id":"version-003","dir":"Changelog","previous_headings":"","what":"version 0.0.3","title":"version 0.0.3","text":"CRAN release: 2024-03-01 Fixing bug related summary grouping variables. Removed export tag internal functions cleaner reference manual, added pkgdown site.","code":""}]