期刊
BIOMETRIKA
卷 96, 期 1, 页码 221-228出版社
OXFORD UNIV PRESS
DOI: 10.1093/biomet/asn073
关键词
Biased sampling; Empirical process; Maximum likelihood estimation; Missing data; Outcome-dependent; Profile likelihood; Two-stage sampling
资金
- U. S. National Institutes of Health
Outcome-dependent sampling designs have been shown to be a cost-effective way to enhance study efficiency. We show that the outcome-dependent sampling design with a continuous outcome can be viewed as an extension of the two-stage case-control designs to the continuous-outcome case. We further show that the two-stage outcome-dependent sampling has a natural link with the missing-data and biased-sampling frameworks. Through the use of semiparametric inference and missing-data techniques, we show that a certain semiparametric maximum-likelihood estimator is computationally convenient and achieves the semiparametric efficient information bound. We demonstrate this both theoretically and through simulation.
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