Semiparametric inference for a 2-stage outcome-auxiliary-dependent sampling design with continuous outcome.
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.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420, USA. zhou@bios.unc.edu
Haibo
H
Zhou
Yuanshan
Y
Wu
Yanyan
Y
Liu
Jianwen
J
Cai
D002648:Child; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007360:Intelligence; D016013:Likelihood Functions; D008297:Male; D018811:Maternal Exposure / Q000009:adverse effects; D015233:Models, Statistical; D011078:Polychlorinated Biphenyls / Q000633:toxicity; D012107:Research Design
D011078:Polychlorinated Biphenyls
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