|Title||Semiparametric inference for a 2-stage outcome-auxiliary-dependent sampling design with continuous outcome.|
|Publication Type||Journal Article|
|Year of Publication||2011|
|Authors||Zhou, Haibo, Yuanshan Wu, Yanyan Liu, and Jianwen Cai|
|Date Published||2011 Jul|
|Keywords||Child, Computer Simulation, Data Interpretation, Statistical, Female, Humans, Intelligence, Likelihood Functions, Male, Maternal Exposure, Models, Statistical, Polychlorinated Biphenyls, Research Design|
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.
|Original Publication||Semiparametric inference for a 2-stage outcome-auxiliary-dependent sampling design with continuous outcome.|
|PubMed Central ID||PMC3114654|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 HL57444 / HL / NHLBI NIH HHS / United States
R01 CA79949 / CA / NCI NIH HHS / United States
Semiparametric inference for a 2-stage outcome-auxiliary-dependent sampling design with continuous outcome.