title: 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.
authors.affiliation: Department of Biostatistics, School of Public Health and Key Laboratory of Public Health Safety, Ministry of Education of China, Fudan University, Shanghai 200032, People's Republic of China.
authors.forename: Guoyou
authors.initials: G
authors.lastname: Qin
authors.forename: Haibo
authors.initials: H
authors.lastname: Zhou
mesh_terms: D002648:Child; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007360:Intelligence; D016013:Likelihood Functions; D016014:Linear Models; D008297:Male; D018811:Maternal Exposure / Q000009:adverse effects; D011078:Polychlorinated Biphenyls / Q000633:toxicity; D012107:Research Design
chemical_list: D011078:Polychlorinated Biphenyls