Efficient estimation for accelerated failure time model under case-cohort and nested case-control sampling.

TitleEfficient estimation for accelerated failure time model under case-cohort and nested case-control sampling.
Publication TypeJournal Article
Year of Publication2017
AuthorsKang, Suhyun, Wenbin Lu, and Mengling Liu
JournalBiometrics
Volume73
Issue1
Pagination114-123
Date Published2017 03
ISSN1541-0420
KeywordsAlgorithms, Case-Control Studies, Computer Simulation, Data Interpretation, Statistical, Healthcare Failure Mode and Effect Analysis, Humans, Likelihood Functions, Models, Statistical, Regression Analysis, Wilms Tumor
Abstract

Case-cohort (Prentice, 1986) and nested case-control (Thomas, 1977) designs have been widely used as a cost-effective alternative to the full-cohort design. In this article, we propose an efficient likelihood-based estimation method for the accelerated failure time model under case-cohort and nested case-control designs. An EM algorithm is developed to maximize the likelihood function and a kernel smoothing technique is adopted to facilitate the estimation in the M-step of the EM algorithm. We show that the proposed estimators for the regression coefficients are consistent and asymptotically normal. The asymptotic variance of the estimators can be consistently estimated using an EM-aided numerical differentiation method. Simulation studies are conducted to evaluate the finite-sample performance of the estimators and an application to a Wilms tumor data set is also given to illustrate the methodology.

DOI10.1111/biom.12573
Alternate JournalBiometrics
Original PublicationEfficient estimation for accelerated failure time model under case-cohort and nested case-control sampling.
PubMed ID27479331
PubMed Central IDPMC5288392
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
P30 CA016087 / CA / NCI NIH HHS / United States
R01 CA140632 / CA / NCI NIH HHS / United States
R21 CA169739 / CA / NCI NIH HHS / United States