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]

BayesCACE_1.2.3.tar.gz
BayesCACE_1.2.3.zip(r-4.7-any)BayesCACE_1.2.3.zip(r-4.6-any)BayesCACE_1.2.3.zip(r-4.5-any)
BayesCACE_1.2.3.tgz(r-4.6-any)BayesCACE_1.2.3.tgz(r-4.5-any)
BayesCACE_1.2.3.tar.gz(r-4.7-any)BayesCACE_1.2.3.tar.gz(r-4.6-any)
BayesCACE_1.2.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
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.

jags cpp

2.00 score 414 downloads 16 exports 26 dependencies

Last updated from:4a9cf5282f. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	144
source / vignettes	OK	161
linux-release-x86_64	OK	141
macos-release-arm64	OK	121
macos-oldrel-arm64	OK	123
windows-devel	OK	106
windows-release	OK	134
windows-oldrel	OK	101
wasm-release	OK	105

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 digest forestplot lattice lme4 MASS mathjaxr Matrix metadat metafor minqa nlme nloptr numDeriv pbapply rbibutils Rcpp RcppEigen Rdpack reformulas rjags rlang

BayesCACE paper

intro | model | apply | discuss

Rendered frommain.pdf.asisusingR.rsp::asis

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

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)