Estimation of a partially linear additive model for data from an outcome-dependent sampling design with a continuous outcome.

TitleEstimation of a partially linear additive model for data from an outcome-dependent sampling design with a continuous outcome.
Publication TypeJournal Article
Year of Publication2016
AuthorsTan, Ziwen, Guoyou Qin, and Haibo Zhou
JournalBiostatistics
Volume17
Issue4
Pagination663-76
Date Published2016 10
ISSN1468-4357
KeywordsChild, Child Development, Data Interpretation, Statistical, Female, Humans, Intelligence, Models, Theoretical, Pregnancy, Prenatal Exposure Delayed Effects, Research Design
Abstract

Outcome-dependent sampling (ODS) designs have been well recognized as a cost-effective way to enhance study efficiency in both statistical literature and biomedical and epidemiologic studies. A partially linear additive model (PLAM) is widely applied in real problems because it allows for a flexible specification of the dependence of the response on some covariates in a linear fashion and other covariates in a nonlinear non-parametric fashion. Motivated by an epidemiological study investigating the effect of prenatal polychlorinated biphenyls exposure on children's intelligence quotient (IQ) at age 7 years, we propose a PLAM in this article to investigate a more flexible non-parametric inference on the relationships among the response and covariates under the ODS scheme. We propose the estimation method and establish the asymptotic properties of the proposed estimator. Simulation studies are conducted to show the improved efficiency of the proposed ODS estimator for PLAM compared with that from a traditional simple random sampling design with the same sample size. The data of the above-mentioned study is analyzed to illustrate the proposed method.

DOI10.1093/biostatistics/kxw015
Alternate JournalBiostatistics
Original PublicationEstimation of a partially linear additive model for data from an outcome-dependent sampling design with a continuous outcome.
PubMed ID27006375
PubMed Central IDPMC5031945
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
P30 ES010126 / ES / NIEHS NIH HHS / United States
R01 ES021900 / ES / NIEHS NIH HHS / United States