On the estimation of average treatment effects with right-censored time to event outcome and competing risks

Brice Maxime Hugues Ozenne, Thomas Harder Scheike, Laila Staerk, Thomas Alexander Gerds

43 Citations (Scopus)

Abstract

We are interested in the estimation of average treatment effects based on right-censored data of an observational study. We focus on causal inference of differences between t-year absolute event risks in a situation with competing risks. We derive doubly robust estimation equations and implement estimators for the nuisance parameters based on working regression models for the outcome, censoring, and treatment distribution conditional on auxiliary baseline covariates. We use the functional delta method to show that these estimators are regular asymptotically linear estimators and estimate their variances based on estimates of their influence functions. In empirical studies, we assess the robustness of the estimators and the coverage of confidence intervals. The methods are further illustrated using data from a Danish registry study.

Original languageEnglish
JournalBiometrical journal. Biometrische Zeitschrift
Volume62
Issue number3
Pages (from-to)751-763
Number of pages13
ISSN0323-3847
DOIs
Publication statusPublished - May 2020

Keywords

  • Cox regression model
  • hazard ratio
  • probabilistic index
  • relative risk
  • survival analysis

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