BCEA: An R package to perform Bayesian Cost-Effectiveness Analysis

BCEA is a R library specifically designed to post-process the result of a Bayesian health economic evaluation. Typically, this consists in the estimation of a set of relevant parameters that can be combined to produce an estimation of suitable measures of cost (\(c\)) and clinical benefits (\(e\)) associated with an intervention. Within the Bayesian framework, this amounts to estimating a posterior distribution for the pair \((e,c)\). Health economic evaluations then proceed by computing some relevant summaries of the resulting decision process: is the innovative intervention \(t=1\) more “cost-effective” than the standard intervention \(t=0\)?

survHE: Survival analysis in health economic evaluation

Survival analysis in health economic evaluation Contains a suite of functions to streamline systematically 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. For a selected range of models, both Integrated Nested Laplace Integration (via the R package INLA) and Hamiltonian Monte Carlo (HMC; via the R package rstan) are possible.

bmeta - Bayesian meta-analysis & meta-regression in R

bmeta is a R package that provides a collection of functions for conducting meta-analyses and meta-regressions under a Bayesian context, using JAGS. The package includes functions for computing various effect size or outcome measures (e.g. odds ratios, mean difference and incidence rate ratio) for different types of data (e.g. binary, continuous and count, respectively), based on Markov Chain Monte Carlo (MCMC) simulations. Users are allowed to select fixed- and random-effects models with different prior distributions for the data and the relevant parameters.

BCEAweb: A web-application to front-end the R package BCEA

BCEAweb is a front-end that can be used to access many of the functionalities of the R package BCEA. It can be accessed here.

SWSamp: Simulation-based sample size calculations for a Stepped Wedge Trial (and more)

Introduction SWSamp is an R package designed to allow a wide range of simulation-based sample size calculations, specifically (but not exclusively!) for a Stepped Wedge Trial (SWT) and is based on the general framework described in Baio et al (2015). In its current version, SWSamp consists of 5 main functions: the first one (which is currently in fact specified by three different commands) performs the analytic sample size calculations using the method of Hussey and Hughes (2007).

INLA (contributed functions)

Integrated Nested Laplace Approximation R-INLA is an R package that implements Integrated Nested Laplace Approximation (INLA), a method to perform approximate Bayesian analysis for a wide class of model specifications, including hierarchical regression models and spatial or spatio-temporal models. The idea underlying INLA is that, instead of performing computation for the posterior or predictive distributions using MCMC (which is generally very effective, but can be very computationally intensive, especially for complex models or very large datasets), in a specific class of models in which the prior distribution for the (vector of) parameter(s) is characterised by Gaussian Random Markov Fields, these tasks can be performed using approximations based on Laplace methods.