Epidemiology modelling framework for the Thanzi la Onse project
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Updated
Aug 20, 2024 - Python
Epidemiology modelling framework for the Thanzi la Onse project
IPECAD open-source model
Health economic simulation modeling and decision analysis
Survival analysis in health economic evaluation Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective.
Tools for building decision models for health technology assessment.
Bayesian Cost Effectiveness Analysis. Given the results of a Bayesian model (possibly based on MCMC) in the form of simulations from the posterior distributions of suitable variables of costs and clinical benefits for two or more interventions, produces a health economic evaluation. Compares one of the interventions (the "reference") to the othe…
Cost-Effectiveness Analysis for Clinical Trials
R package that facilitates performing matching-adjusted indirect comparison (MAIC) anaylsis for a disconnected treatment network where the endpoint of interest is either time-to-event (e.g. overall survival) or binary (e.g. objective tumor response).
I am a health economist currently funded by the Wellcome Trust and the University of Sheffield.
R package that provides models and functions to fit non-standard NMAs: piecewise exponential models and fractional polynomial models for grouped survival data (derived from digitized Kaplan-Meier curves).
R package that facilitates performing survival analysis to support the creation of Excel based economic models by providing helper functions to use alongside the flexsurv package.
Survival analysis in health economic evaluation using Bayesian modelling though Integrated Nested Laplace Approximation. Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation.
Health economic evaluations from individual level data with missing values using a set of pre-defined Bayesian models written in BUGS. A series of parametric models are available to jointly model partially-observed effectiveness and cost outcomes under both ignorable and nonignroable missing data mechanism assumptions
R package that facilitates the use of discrete event simulations without resource constraints for cost-effectiveness analysis.
R package that extends the rpsftm package with some useful default reports and functions to facilitate sensitivity analysis. Covering descriptive, diagnostic and reporting aspects.
📈Markov Models for Health Economic Evaluations
Collection of R Packages that support analysis for the purposes of Health Technology Assessment (HTA)
A suite of functions for the calculation and presentation of the Expected Value of Sample Information.
Speed up Discrete Markov Model Simulations.
PSA-ReD is a novel approach to the traditional scatterplot for displaying the results of Probabilistic Sensitivity Analysis. Here, we provide the script and an example dataset so that everyone can use this new and improved approach. This new approach is described in detail in the paper titled "Increasing the information provided by probabilistic…
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