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 |
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