Semiparametric regression analysis of doubly censored failure time data from cohort studies

Shuwei Li, Jianguo Sun, Tian Tian and Xia Cui ()
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Shuwei Li: Guangzhou University
Jianguo Sun: University of Missouri
Tian Tian: University of Missouri
Xia Cui: Guangzhou University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2020, vol. 26, issue 2, No 5, 315-338

Abstract: Abstract Doubly censored failure time data occur when the failure time of interest represents the elapsed time between two events, an initial event and a subsequent event, and the observations on both events may suffer censoring. A well-known example of such data is given by the acquired immune deficiency syndrome (AIDS) cohort study in which the two events are HIV infection and AIDS diagnosis, and several inference methods have been developed in the literature for their regression analysis. However, all of them only apply to limited situations or focus on a single model. In this paper, we propose a marginal likelihood approach based on a general class of semiparametric transformation models, which can be applied to much more general situations. For the implementation, we develop a two-step procedure that makes use of both the multiple imputation technique and a novel EM algorithm. The asymptotic properties of the resulting estimators are established by using the modern empirical process theory, and the simulation study conducted suggests that the method works well in practical situations. An application is also provided.

Keywords: Double censoring; EM algorithm; Multiple imputation; Semiparametric transformation models (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10985-019-09477-x

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