Regression analysis of doubly censored failure time data with ancillary information

Mingyue Du (), Xiyuan Gao and Ling Chen
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Mingyue Du: Jilin University
Xiyuan Gao: University of Missouri
Ling Chen: Washington University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2024, vol. 30, issue 3, No 8, 667-679

Abstract: Abstract Doubly censored failure time data occur in many areas and for the situation, the failure time of interest usually represents the elapsed time between two related events such as an infection and the resulting disease onset. Although many methods have been proposed for regression analysis of such data, most of them are conditional on the occurrence time of the initial event and ignore the relationship between the two events or the ancillary information contained in the initial event. Corresponding to this, a new sieve maximum likelihood approach is proposed that makes use of the ancillary information, and in the method, the logistic model and Cox proportional hazards model are employed to model the initial event and the failure time of interest, respectively. A simulation study is conducted and suggests that the proposed method works well in practice and is more efficient than the existing methods as expected. The approach is applied to an AIDS study that motivated this investigation.

Keywords: Ancillary information; Cox proportional hazards model; Doubly Censored data; Logistic model (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10985-024-09625-y

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