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

Author

Listed:
  • Zhang Zhiwei
  • Ma Shujie

    (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)

  • Zhang Min

    (University of Michigan, Department of Biostatistics, Ann Arbor, United States)

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.

Suggested Citation

  • Zhang Zhiwei & Ma Shujie & Liu Chunling & Zhang Min, 2019. "Estimating Mann–Whitney-Type Causal Effects for Right-Censored Survival Outcomes," Journal of Causal Inference, De Gruyter, vol. 7(1), pages 1-15, March.
    • Handle: RePEc:bpj:causin:v:7:y:2019:i:1:p:15:n:1
    DOI: 10.1515/jci-2018-0010
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    References listed on IDEAS

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    1. Mark J. Laan & Alan Hubbard, 1999. "Locally Efficient Estimation of the Quality-Adjusted Lifetime Distribution with Right-Censored Data and Covariates," Biometrics, The International Biometric Society, vol. 55(2), pages 530-536, June.
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