Doubly-robust estimators of treatment-specific survival distributions in observational studies with stratified sampling.
Published
Journal Article
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.
Full Text
Duke Authors
Cited Authors
- Bai, X; Tsiatis, AA; O'Brien, SM
Published Date
- December 2013
Published In
Volume / Issue
- 69 / 4
Start / End Page
- 830 - 839
PubMed ID
- 24117096
Pubmed Central ID
- 24117096
Electronic International Standard Serial Number (EISSN)
- 1541-0420
Digital Object Identifier (DOI)
- 10.1111/biom.12076
Language
- eng
Conference Location
- United States