A flexible approach for causal inference with multiple treatments and clustered survival outcomes.

ABSTRACT: When drawing causal inferences about the effects of multiple treatments on clustered survival outcomes using observational data, we need to address implications of the multilevel data structure, multiple treatments, censoring, and unmeasured confounding for causal analyses. Few off-the-shelf causal inference tools are available to simultaneously tackle these issues. We develop a flexible random-intercept accelerated failure time model, in which we use Bayesian additive regression trees to capture arbitrarily complex relationships between censored survival times and pre-treatment covariates and use the random intercepts to capture cluster-specific main effects. We develop an efficient Markov chain Monte Carlo algorithm to draw posterior inferences about the population survival effects of mu

SUBMITTER: Hu L 

PROVIDER: S-EPMC9588538 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature