Doubly-robust estimators of treatment-specific survival distributions in observational studies with stratified sampling

Biometrics. 2013 Dec;69(4):830-9. doi: 10.1111/biom.12076. Epub 2013 Oct 11.


Observational studies are frequently conducted to compare the effects of two treatments on survival. For such studies we must be concerned about confounding; that is, there are covariates that affect both the treatment assignment and the survival distribution. With confounding the usual treatment-specific Kaplan-Meier estimator might be a biased estimator of the underlying treatment-specific survival distribution. This article has two aims. In the first aim we use semiparametric theory to derive a doubly robust estimator of the treatment-specific survival distribution in cases where it is believed that all the potential confounders are captured. In cases where not all potential confounders have been captured one may conduct a substudy using a stratified sampling scheme to capture additional covariates that may account for confounding. The second aim is to derive a doubly-robust estimator for the treatment-specific survival distributions and its variance estimator with such a stratified sampling scheme. Simulation studies are conducted to show consistency and double robustness. These estimators are then applied to the data from the ASCERT study that motivated this research.

Keywords: Cox proportional hazard model; Double robustness; Observational study; Stratified sampling; Survival analysis.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Coronary Artery Disease / mortality*
  • Coronary Artery Disease / surgery*
  • Data Interpretation, Statistical*
  • Humans
  • Observational Studies as Topic / methods*
  • Outcome Assessment, Health Care / methods*
  • Prevalence
  • Reproducibility of Results
  • Sample Size
  • Sensitivity and Specificity
  • Statistical Distributions
  • Survival Analysis*
  • Treatment Outcome
  • United States / epidemiology