Package: BayesCACE 1.2.3

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.

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

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BayesCACE.pdf |BayesCACE.html✨
BayesCACE/json (API)

# Install 'BayesCACE' in R:

install.packages('BayesCACE', repos = c('https://formidify.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

jags– Just Another Gibbs Sampler for Bayesian MCMC
c++– GNU Standard C++ Library v3

Datasets:

epidural_c - Meta-analysis data with full compliance information
epidural_ic - Meta-analysis data without full compliance information

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 363 downloads 16 exports 23 dependencies

Last updated 2 years agofrom:4a9cf5282f. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 21 2024
R-4.5-win	OK	Nov 21 2024
R-4.5-linux	OK	Nov 21 2024
R-4.4-win	OK	Nov 21 2024
R-4.4-mac	OK	Nov 21 2024
R-4.3-win	OK	Nov 21 2024
R-4.3-mac	OK	Nov 21 2024

Exports:cace.meta.c cace.meta.ic cace.study coda.names coda.samples.dic model.meta.c model.meta.ic model.study parse.varname plt.acf plt.density plt.forest plt.noncomp plt.trace prior.meta prior.study

Dependencies:abind backports boot checkmate coda forestplot lattice lme4 MASS mathjaxr Matrix metadat metafor minqa nlme nloptr numDeriv pbapply rbibutils Rcpp RcppEigen Rdpack rjags

BayesCACE paper

Rendered frommain.pdf.asisusingR.rsp::asison Nov 21 2024.

Last update: 2022-01-05
Started: 2022-01-05

Help page	Topics
Bayesian hierarchical models for CACE meta-analysis with complete compliance data	cace.meta.c
Bayesian hierarchical models for CACE meta-analysis with incomplete compliance information	cace.meta.ic
CACE analysis for a single study, or a two-step approach for meta-analysis with complete complice information	cace.study
Get names of node array	coda.names
Generate posterior samples in mcmc.list format	coda.samples.dic
Meta-analysis data with full compliance information	epidural_c
Meta-analysis data without full compliance information	epidural_ic
Bayesian hierarchical model code for CACE meta-analysis with complete compliance data	model.meta.c
Bayesian hierarchical model code for CACE meta-analysis with complete compliance data	model.meta.ic
Model code of CACE analysis for a single study, or a two-step approach for meta-analysis with complete complice information	model.study
Parse strings of specific form	parse.varname
this plot function creates an acf plot	plt.acf
this plot function creates a density plot	plt.density
this plot function makes a forest plot.	plt.forest
Plotting noncompliance rates for a given dataset	plt.noncomp
this plot function creates a traceplot	plt.trace
The function returns a custom string that specifies part of the model.	prior.meta
The function returns a custom string that specifies part of the model (single-study).	prior.study

Package: BayesCACE 1.2.3

BayesCACE: Bayesian Model for CACE Analysis

BayesCACE paper

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)