Bayesian Model for CACE Analysis

Performs CACE (Complier Average Causal Effect analysis) on either a single study or meta-analysis of datasets with binary outcomes, using either complete or incomplete noncompliance information. Our package implements the Bayesian methods proposed in Zhou et al. (2019) , which introduces a Bayesian hierarchical model for estimating CACE in meta-analysis of clinical trials with noncompliance, and Zhou et al. (2021) , with an application example on Epidural Analgesia.


Reference manual

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install.packages("BayesCACE")

1.2.3 by Jinhui Yang, 4 years ago


Browse source code at https://github.com/cran/BayesCACE


Authors: Jinhui Yang [aut, cre] , Jincheng Zhou [aut] , James Hodges [ctb] , Haitao Chu [ctb]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports coda, Rdpack, grDevices, forestplot, metafor, lme4, methods

Depends on rjags

Suggests R.rsp

System requirements: JAGS 4.x.y (http://mcmc-jags.sourceforge.net)


See at CRAN