Semiparametric inference for a 2-stage outcome-auxiliary-dependent sampling design with continuous outcome.

TitleSemiparametric inference for a 2-stage outcome-auxiliary-dependent sampling design with continuous outcome.
Publication TypeJournal Article
Year of Publication2011
AuthorsZhou, Haibo, Yuanshan Wu, Yanyan Liu, and Jianwen Cai
JournalBiostatistics
Volume12
Issue3
Pagination521-34
Date Published2011 Jul
ISSN1468-4357
KeywordsChild, Computer Simulation, Data Interpretation, Statistical, Female, Humans, Intelligence, Likelihood Functions, Male, Maternal Exposure, Models, Statistical, Polychlorinated Biphenyls, Research Design
Abstract

Two-stage design has long been recognized to be a cost-effective way for conducting biomedical studies. In many trials, auxiliary covariate information may also be available, and it is of interest to exploit these auxiliary data to improve the efficiency of inferences. In this paper, we propose a 2-stage design with continuous outcome where the second-stage data is sampled with an "outcome-auxiliary-dependent sampling" (OADS) scheme. We propose an estimator which is the maximizer for an estimated likelihood function. We show that the proposed estimator is consistent and asymptotically normally distributed. The simulation study indicates that greater study efficiency gains can be achieved under the proposed 2-stage OADS design by utilizing the auxiliary covariate information when compared with other alternative sampling schemes. We illustrate the proposed method by analyzing a data set from an environmental epidemiologic study.

DOI10.1093/biostatistics/kxq080
Alternate JournalBiostatistics
Original PublicationSemiparametric inference for a 2-stage outcome-auxiliary-dependent sampling design with continuous outcome.
PubMed ID21252082
PubMed Central IDPMC3114654
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 HL57444 / HL / NHLBI NIH HHS / United States
R01 CA79949 / CA / NCI NIH HHS / United States
Project: