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

AI Article Synopsis

  • The paper discusses a cost-effective two-stage design for biomedical studies that uses auxiliary covariate information to enhance the efficiency of inferences.
  • It introduces an "outcome-auxiliary-dependent sampling" (OADS) method for the second stage, proposing an estimator based on an estimated likelihood function.
  • The results show that the OADS design improves study efficiency compared to other sampling methods, validated through simulation and a case study from environmental epidemiology.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3114654PMC
http://dx.doi.org/10.1093/biostatistics/kxq080DOI Listing

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