BayesCACE: 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) <doi:10.1111/biom.13028>, which introduces a Bayesian hierarchical model for estimating CACE in meta-analysis of clinical trials with noncompliance, and Zhou et al. (2021) <doi:10.1080/01621459.2021.1900859>, with an application example on Epidural Analgesia.

Getting started

Package details

AuthorJinhui Yang [aut, cre] (<https://orcid.org/0000-0001-8322-1121>), Jincheng Zhou [aut] (<https://orcid.org/0000-0003-2641-2495>), James Hodges [ctb], Haitao Chu [ctb] (<https://orcid.org/0000-0003-0932-598X>)
MaintainerJinhui Yang <james.yangjinhui@gmail.com>
LicenseGPL (>= 2)
Version1.2
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("BayesCACE")

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BayesCACE documentation built on Jan. 6, 2022, 5:08 p.m.