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.
|Author||Chenguang Wang [aut, cre], Ravi Varadhan [aut], Trustees of Columbia University [cph] (tools/make_cpp.R, R/stanmodels.R)|
|Date of publication||2017-04-23 07:10:02 UTC|
|Maintainer||Chenguang Wang <email@example.com>|
|License||GPL (>= 3)|
beanz-package: Bayesian Approaches for HTE Analysis
bzCallStan: Call STAN models
bzComp: Comparison of posterior treatment effects
bzGailSimon: Gail-Simon Test
bzGetSubgrp: Get subgroup treatment effect estimation and variance
bzGetSubgrpRaw: Get subgroup treatment effect estimation and variance
bzPredSubgrp: Predictive Distribution
bzRptTbl: Summary table of treatment effects
bzShiny: Run Web-Based BEANZ application
bzSummary: Posterior subgroup treatment effects
solvd.sub: Subject level data from SOLVD trial
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