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Multiple comparisons for survival data with propensity score adjustment

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  • Zhu, Hong
  • Lu, Bo

Abstract

This article considers the practical problem in clinical and observational studies where multiple treatment or prognostic groups are compared and the observed survival data are subject to right censoring. Two possible formulations of multiple comparisons are suggested. Multiple Comparisons with a Control (MCC) compare every other group to a control group with respect to survival outcomes, for determining which groups are associated with lower risk than the control. Multiple Comparisons with the Best (MCB) compare each group to the truly minimum risk group and identify the groups that are either with the minimum risk or the practically minimum risk. To make a causal statement, potential confounding effects need to be adjusted in the comparisons. Propensity score based adjustment is popular in causal inference and can effectively reduce the confounding bias. Based on a propensity-score-stratified Cox proportional hazards model, the approaches of MCC test and MCB simultaneous confidence intervals for general linear models with normal error outcome are extended to survival outcome. This paper specifies the assumptions for causal inference on survival outcomes within a potential outcome framework, develops testing procedures for multiple comparisons and provides simultaneous confidence intervals. The proposed methods are applied to two real data sets from cancer studies for illustration, and a simulation study is also presented.

Suggested Citation

  • Zhu, Hong & Lu, Bo, 2015. "Multiple comparisons for survival data with propensity score adjustment," Computational Statistics & Data Analysis, Elsevier, vol. 86(C), pages 42-51.
    • Handle: RePEc:eee:csdana:v:86:y:2015:i:c:p:42-51
    DOI: 10.1016/j.csda.2015.01.001
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    References listed on IDEAS

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    1. Lu B. & Zanutto E. & Hornik R. & Rosenbaum P.R., 2001. "Matching With Doses in an Observational Study of a Media Campaign Against Drug Abuse," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1245-1253, December.
    2. Kosuke Imai & David A. van Dyk, 2004. "Causal Inference With General Treatment Regimes: Generalizing the Propensity Score," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 854-866, January.
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