Title | Efficient estimation for accelerated failure time model under case-cohort and nested case-control sampling. |
Publication Type | Journal Article |
Year of Publication | 2017 |
Authors | Kang, Suhyun, Wenbin Lu, and Mengling Liu |
Journal | Biometrics |
Volume | 73 |
Issue | 1 |
Pagination | 114-123 |
Date Published | 2017 Mar |
ISSN | 1541-0420 |
Keywords | Algorithms, 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. |
DOI | 10.1111/biom.12573 |
Alternate Journal | Biometrics |
Original Publication | Efficient estimation for accelerated failure time model under case-cohort and nested case-control sampling. |
PubMed ID | 27479331 |
PubMed Central ID | PMC5288392 |
Grant List | P01 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 |
Efficient estimation for accelerated failure time model under case-cohort and nested case-control sampling.
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