2019/07/30 by Brice Maxime Hugues Ozenne, Thomas Harder Scheike, Laila Stærk +1 · 76 citations
Mathematics · #Advanced Causal Inference Techniques #Causal inference #Conditional probability distribution #Confidence interval #Estimation #Estimator #Inference #Nuisance parameter #Observational study #Robustness (evolution) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistical inference #stat.ME
paper · pdf · doi:10.1002/bimj.201800298
published in Biometrical Journal 62(3), 751-763 (Wiley)
arxiv created 2019/07/30 · openalex publication_date 2020/02/11 · openalex created_date 2020/02/24 · arxiv updated 2020/03/17 · openalex updated_date 2026/08/05
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.