Semiparametric Inference for Data with a Continuous Outcome from a Two-Phase Probability Dependent Sampling Scheme.

TitleSemiparametric Inference for Data with a Continuous Outcome from a Two-Phase Probability Dependent Sampling Scheme.
Publication TypeJournal Article
Year of Publication2014
AuthorsZhou, Haibo, Wangli Xu, Donglin Zeng, and Jianwen Cai
JournalJ R Stat Soc Series B Stat Methodol
Volume76
Issue1
Pagination197-215
Date Published2014 Jan 01
ISSN1369-7412
Abstract

Multi-phased designs and biased sampling designs are two of the well recognized approaches to enhance study efficiency. In this paper, we propose a new and cost-effective sampling design, the two-phase probability dependent sampling design (PDS), for studies with a continuous outcome. This design will enable investigators to make efficient use of resources by targeting more informative subjects for sampling. We develop a new semiparametric empirical likelihood inference method to take advantage of data obtained through a PDS design. Simulation study results indicate that the proposed sampling scheme, coupled with the proposed estimator, is more efficient and more powerful than the existing outcome dependent sampling design and the simple random sampling design with the same sample size. We illustrate the proposed method with a real data set from an environmental epidemiologic study.

DOI10.1111/rssb.12029
Alternate JournalJ R Stat Soc Series B Stat Methodol
Original PublicationSemiparametric inference for data with a continuous outcome from a two-phase probability dependent sampling scheme.
PubMed ID24737947
PubMed Central IDPMC3984585
Grant ListR01 CA082659 / CA / NCI NIH HHS / United States
UL1 TR001111 / TR / NCATS NIH HHS / United States
R37 GM047845 / GM / NIGMS NIH HHS / United States
UL1 RR025747 / RR / NCRR NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 ES021900 / ES / NIEHS NIH HHS / United States
R01 CA079949 / CA / NCI NIH HHS / United States
Project: