期刊
BIOMETRIKA
卷 102, 期 3, 页码 739-746出版社
OXFORD UNIV PRESS
DOI: 10.1093/biomet/asv025
关键词
Biased sampling; Doubly robust; Effect on the treated; Efficient influence function; Study design
资金
- U.S. National Institutes of Health
- Swedish Research Council
- U.S. National Science Foundation
- Direct For Social, Behav & Economic Scie
- Divn Of Social and Economic Sciences [1260782] Funding Source: National Science Foundation
Odds ratios can be estimated in case-control studies using standard logistic regression, ignoring the outcome-dependent sampling. In this paper we discuss an analogous result for treatment effects on the treated in matched cohort studies. Specifically, in studies where a sample of treated subjects is observed along with a separate sample of possibly matched controls, we show that efficient and doubly robust estimators of effects on the treated are computationally equivalent to standard estimators, which ignore the matching and exposure-based sampling. This is not the case for general average effects. We also show that matched cohort studies are often more efficient than random sampling for estimating effects on the treated, and derive the optimal number of matches for a given set of matching variables. We illustrate our results via simulation and in a matched cohort study of the effect of hysterectomy on the risk of cardiovascular disease.
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