Efficient Estimation of Semiparametric Transformation Models for Two-Phase Cohort Studies
Donglin Zeng and
D. Y. Lin
Journal of the American Statistical Association, 2014, vol. 109, issue 505, 371-383
Abstract:
Under two-phase cohort designs, such as case--cohort and nested case--control sampling, information on observed event times, event indicators, and inexpensive covariates is collected in the first phase, and the first-phase information is used to select subjects for measurements of expensive covariates in the second phase; inexpensive covariates are also used in the data analysis to control for confounding and to evaluate interactions. This article provides efficient estimation of semiparametric transformation models for such designs, accommodating both discrete and continuous covariates, and allowing inexpensive and expensive covariates to be correlated. The estimation is based on the maximization of a modified nonparametric likelihood function through a generalization of the expectation--maximization algorithm. The resulting estimators are shown to be consistent, asymptotically normal and asymptotically efficient with easily estimated variances. Simulation studies demonstrate that the asymptotic approximations are accurate in practical situations. Empirical data from Wilms' tumor studies and the Atherosclerosis Risk in Communities (ARIC) study are presented. Supplementary materials for this article are available online.
Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (15)
Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2013.842172 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:109:y:2014:i:505:p:371-383
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20
DOI: 10.1080/01621459.2013.842172
Access Statistics for this article
Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson
More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().