Varying coefficient transformation cure models for failure time data

Man-Hua Chen () and Xingwei Tong
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Man-Hua Chen: Tamkang University
Xingwei Tong: Beijing Normal University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2020, vol. 26, issue 3, No 5, 518-544

Abstract: Abstract This article discusses regression analysis of right-censored failure time data where there may exist a cured subgroup, and also covariate effects may be varying with time, a phenomena that often occurs in many medical studies. To address the problem, we discuss a class of varying coefficient transformation models along with a logistic model for the cured subgroup. For inference, a sieve maximum likelihood approach is developed with the use of spline functions, and the asymptotic properties of the proposed estimators are established. The proposed method can be easily implemented, and the conducted simulation study suggests that the proposed method works well in practical situations. An illustrative example is provided.

Keywords: Cure model; Maximum likelihood estimation; Regression analysis; Spline smoothing (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10985-019-09488-8

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