Statistics and Its Interface

Volume 11 (2018)

Number 4

Case-cohort design for accelerated hazards model

Pages: 657 – 668

DOI: https://dx.doi.org/10.4310/SII.2018.v11.n4.a10

Authors

Jieli Ding (School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei, China)

Xiaolong Chen (School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei, China)

Huaying Fang (School of Mathematical Sciences and Center for Quantitative Biology, Peking University, Beijing, China)

Yanyan Liu (School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei, China)

Abstract

Case-cohort design is widely used in biomedical studies of rare diseases as an efficient way to reduce cost. Relevant covariate histories, which are costly or difficult to obtain, are observed only on cases and a random subcohort in such studies. It is often that a lag period exists before the treatment or other covariates is fully effective. This phenomenon may be described well by an accelerated hazards model. Existing methods for the accelerated hazards model do not handle case-cohort data. This paper proposes a semiparametric inference method for the accelerated hazards model with data from a case-cohort design. The proposed estimators are shown to be consistent and asymptotically normally distributed. The finite sample properties of proposed case-cohort estimator and its relative efficiency to full cohort estimator are assessed via simulation studies. An application to a burn study demonstrates the utility of the proposed method in practice.

Keywords

case-cohort design, accelerated hazards model, estimating equation

2010 Mathematics Subject Classification

Primary 62D05, 62Nxx. Secondary 62N01, 62N02.

Full Text (PDF format)

The research of J. Ding is supported by the National Science Foundation of China grant 11671310.

The research of Y. Liu is supported by the National Science Foundation of China grants 11571263 and 11371299.

Received 19 May 2017

Published 19 September 2018