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

Content Provider	Europe PMC
Author	Hu, Liangyuan Ji, Jiayi Ennis, Ronald D. Hogan, Joseph W.
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 multiple treatments and examine the variability in cluster‐level effects. We further propose an interpretable sensitivity analysis approach to evaluate the sensitivity of drawn causal inferences about treatment effect to the potential magnitude of departure from the causal assumption of no unmeasured confounding. Expansive simulations empirically validate and demonstrate good practical operating characteristics of our proposed methods. Applying the proposed methods to a dataset on older high‐risk localized prostate cancer patients drawn from the National Cancer Database, we evaluate the comparative effects of three treatment approaches on patient survival, and assess the ramifications of potential unmeasured confounding. The methods developed in this work are readily available in the Rpackage riAFTBART.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9588538&blobtype=pdf
Page Count	18
ISSN	02776715
Journal	Statistics in Medicine [Stat Med]
Volume Number	41
DOI	10.1002/sim.9548
PubMed Central reference number	PMC9588538
Issue Number	25
PubMed reference number	35948011
e-ISSN	10970258
Language	English
Publisher	John Wiley & Sons, Inc.
Publisher Date	2022-08-10
Publisher Place	Hoboken, USA
Access Restriction	Open
Rights License	This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Subject Keyword	Bayesian machine learning multilevel survival data observational studies sensitivity analysis
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Epidemiology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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