riAFTBART: A Flexible Approach for Causal Inference with Multiple Treatments and Clustered Survival Outcomes

Random-intercept accelerated failure time (AFT) model utilizing Bayesian additive regression trees (BART) for drawing causal inferences about multiple treatments while accounting for the multilevel survival data structure. It also includes an interpretable sensitivity analysis approach to evaluate how the drawn causal conclusions might be altered in response to the potential magnitude of departure from the no unmeasured confounding assumption.This package implements the methods described by Hu et al. (2022) <doi:10.1002/sim.9548>.

Version:	0.3.3
Imports:	MCMCpack, msm, dbarts, magrittr, foreach, doParallel, dplyr, BART, stringr, tidyr, survival, cowplot, ggplot2, twang, nnet, RRF, randomForest
Published:	2024-05-29
DOI:	10.32614/CRAN.package.riAFTBART
Author:	Liangyuan Hu [aut], Jiayi Ji [aut], Fengrui Zhang [cre]
Maintainer:	Fengrui Zhang <fz174 at sph.rutgers.edu>
License:	MIT + file LICENSE
NeedsCompilation:	no
CRAN checks:	riAFTBART results

Documentation:

Reference manual:

riAFTBART.pdf

Downloads:

Package source:	riAFTBART_0.3.3.tar.gz
Windows binaries:	r-devel: riAFTBART_0.3.3.zip, r-release: riAFTBART_0.3.3.zip, r-oldrel: riAFTBART_0.3.3.zip
macOS binaries:	r-release (arm64): riAFTBART_0.3.3.tgz, r-oldrel (arm64): riAFTBART_0.3.3.tgz, r-release (x86_64): riAFTBART_0.3.3.tgz, r-oldrel (x86_64): riAFTBART_0.3.3.tgz
Old sources:	riAFTBART archive

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