It is vital to assess the heterogeneity of treatment effects
(HTE) when making health care decisions for an individual patient or a group
of patients. Nevertheless, it remains challenging to evaluate HTE based
on information collected from clinical studies that are often designed and
conducted to evaluate the efficacy of a treatment for the overall population.
The Bayesian framework offers a principled and flexible approach to estimate
and compare treatment effects across subgroups of patients defined by their
characteristics. This package allows users to explore a wide range of Bayesian
HTE analysis models, and produce posterior inferences about HTE. See Wang et al.
(2018)
Added a NEWS.md file to track changes to the package.
Added unit test
Made it consistent throughout the software and the manuscript that the half-normal prior is for $\omega$ rather than $\omega^{2}$
Added initial step-size as an option of the sampler
Added adapt-delta as an option in the software and set the default value to 0.95
Added explicit report of Rhat in the results and warnings for problematic convergence based on Rhat summaries
Removed in shiny the option HMC vs. Fixed-params
The Stan models are optimized by vectorization and non-centered parameterization
Updated the GUI to allow the user to enter numerical values directly for priors
Added looic as the measure of goodness of fit and the basis for model comparison
Renamed the parameters in lst.par.pri to match with the model instruction page
Added line for generating results for no subgroup effect model in relevant example code
Added model information to the output from function r.rpt.tbl