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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.
| Version: |
1.2.3 |
| Depends: |
R (≥ 3.5.0), rjags (≥ 4-6) |
| Imports: |
coda, Rdpack, grDevices, forestplot, metafor, lme4, methods |
| Suggests: |
R.rsp |
| Published: |
2022-10-02 |
| Author: |
Jinhui Yang  [aut,
cre],
Jincheng Zhou
[aut],
James Hodges [ctb],
Haitao Chu [ctb] |
| Maintainer: |
Jinhui Yang <james.yangjinhui at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| SystemRequirements: |
JAGS 4.x.y (http://mcmc-jags.sourceforge.net) |
| In views: |
Bayesian |
| CRAN checks: |
BayesCACE results |
Documentation:
Downloads:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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