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Partial linear inference for a 2-stage outcome-dependent sampling design with a continuous outcome.

ABSTRACT: The outcome-dependent sampling (ODS) design, which allows observation of exposure variable to depend on the outcome, has been shown to be cost efficient. In this article, we propose a new statistical inference method, an estimated penalized likelihood method, for a partial linear model in the setting of a 2-stage ODS with a continuous outcome. We develop the asymptotic properties and conduct simulation studies to demonstrate the performance of the proposed estimator. A real environmental study data set is used to illustrate the proposed method.

SUBMITTER: Qin G

PROVIDER: S-EPMC3114650 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Partial linear inference for a 2-stage outcome-dependent sampling design with a continuous outcome.

Qin Guoyou G Zhou Haibo H

Biostatistics (Oxford, England) 20101214 3

The outcome-dependent sampling (ODS) design, which allows observation of exposure variable to depend on the outcome, has been shown to be cost efficient. In this article, we propose a new statistical inference method, an estimated penalized likelihood method, for a partial linear model in the setting of a 2-stage ODS with a continuous outcome. We develop the asymptotic properties and conduct simulation studies to demonstrate the performance of the proposed estimator. A real environmental study d ...[more]

PMID: 21156990

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