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

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...

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Veröffentlicht in:	Statistics in medicine 2022-11, Vol.41 (25), p.4982-4999
Hauptverfasser:	Hu, Liangyuan, Ji, Jiayi, Ennis, Ronald D., Hogan, Joseph W.
Format:	Artikel
Sprache:	eng
Schlagworte:	Bayes Theorem Bayesian machine learning Causality Confounding Factors, Epidemiologic Humans Male Markov Chains Medical research Medical statistics Monte Carlo Method multilevel survival data Observational studies Prostate cancer sensitivity analysis Statistical analysis
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