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Biometrika Advance Access originally published online on January 26, 2009
Biometrika 2009 96(1):221-228; doi:10.1093/biomet/asn073
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© 2009 Biometrika Trust

Miscellanea

A note on semiparametric efficient inference for two-stage outcome-dependent sampling with a continuous outcome

Rui Song, Haibo Zhou and Michael R. Kosorok

Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599-7420, U.S.A. rsong{at}bios.unc.edu zhou{at}bios.unc.edu kosorok{at}unc.edu

Received for publication 1 June 2007. Revision received 1 August 2008.
  Abstract

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.

Key Words: Biased sampling • Empirical process • Maximum likelihood estimation • Missing data • Outcome-dependent • Profile likelihood • Two-stage sampling


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