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BayesCACE (version 1.2.3)

Bayesian Model for CACE Analysis

Description

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.

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Version

Install

install.packages('BayesCACE')

Monthly Downloads

304

Version

1.2.3

License

GPL (>= 2)

Maintainer

Jinhui Yang

Last Published

October 2nd, 2022

Functions in BayesCACE (1.2.3)

parse.varname

Parse strings of specific form
plt.noncomp

Plotting noncompliance rates for a given dataset
plt.acf

this plot function creates an acf plot
plt.trace

this plot function creates a traceplot
prior.meta

The function returns a custom string that specifies part of the model.
prior.study

The function returns a custom string that specifies part of the model (single-study).
cace.study

CACE analysis for a single study, or a two-step approach for meta-analysis with complete complice information
epidural_ic

Meta-analysis data without full compliance information
coda.names

Get names of node array
coda.samples.dic

Generate posterior samples in mcmc.list format
cace.meta.ic

Bayesian hierarchical models for CACE meta-analysis with incomplete compliance information
model.meta.ic

Bayesian hierarchical model code for CACE meta-analysis with complete compliance data
cace.meta.c

Bayesian hierarchical models for CACE meta-analysis with complete compliance data
model.meta.c

Bayesian hierarchical model code for CACE meta-analysis with complete compliance data
model.study

Model code of CACE analysis for a single study, or a two-step approach for meta-analysis with complete complice information
epidural_c

Meta-analysis data with full compliance information
plt.density

this plot function creates a density plot
plt.forest

this plot function makes a forest plot.