Estimating Mann–Whitney-Type Causal Effects for Right-Censored Survival Outcomes

Zhang Zhiwei (), Liu Chunling (), Ma Shujie () and Zhang Min ()
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Zhang Zhiwei: University of California, Riverside, Department of Statistics, 900 University Ave, Riverside, United States
Liu Chunling: Hong Kong Polytechnic University, Department of Applied Mathematics, Hong Kong, China
Ma Shujie: University of California, Riverside, Department of Statistics, 900 University Ave, Riverside, United States
Zhang Min: University of Michigan, Department of Biostatistics, Ann Arbor, United States

Journal of Causal Inference, 2019, vol. 7, issue 1, 15

Abstract: Mann–Whitney-type causal effects are clinically relevant, easy to interpret, and readily applicable to a wide range of study settings. This article considers estimation of such effects when the outcome variable is a survival time subject to right censoring. We derive and discuss several methods: an outcome regression method based on a regression model for the survival outcome, an inverse probability weighting method based on models for treatment assignment and censoring, and two doubly robust methods that involve both types of models and that remain valid under correct specification of the outcome model or the other two models. The methods are compared in a simulation study and applied to an observational study of hospitalized pneumonia.

Keywords: confounding; coarsening; double robustness; time to event; treatment comparison (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:7:y:2019:i:1:p:15:n:1

DOI: 10.1515/jci-2018-0010

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