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Varying coefficient transformation cure models for failure time data

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  • Man-Hua Chen

    (Tamkang University)

  • Xingwei Tong

    (Beijing Normal University)

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.

Suggested Citation

  • Man-Hua Chen & Xingwei Tong, 2020. "Varying coefficient transformation cure models for failure time data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 518-544, July.
    • Handle: RePEc:spr:lifeda:v:26:y:2020:i:3:d:10.1007_s10985-019-09488-8
    DOI: 10.1007/s10985-019-09488-8
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    References listed on IDEAS

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    7. Chen, Chyong-Mei & Lu, Tai-Fang C. & Hsu, Chao-Min, 2013. "Association estimation for clustered failure time data with a cure fraction," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 210-222.
    8. Li, Yi & Lin, Xihong, 2006. "Semiparametric Normal Transformation Models for Spatially Correlated Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 591-603, June.
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