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Proportional exponentiated link transformed hazards (ELTH) models for discrete time survival data with application

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  • Hee-Koung Joeng

    (University of Connecticut)

  • Ming-Hui Chen

    (University of Connecticut)

  • Sangwook Kang

    (Yonsei University)

Abstract

Discrete survival data are routinely encountered in many fields of study including behavior science, economics, epidemiology, medicine, and social science. In this paper, we develop a class of proportional exponentiated link transformed hazards (ELTH) models. We carry out a detailed examination of the role of links in fitting discrete survival data and estimating regression coefficients. Several interesting results are established regarding the choice of links and baseline hazards. We also characterize the conditions for improper survival functions and the conditions for existence of the maximum likelihood estimates under the proposed ELTH models. An extensive simulation study is conducted to examine the empirical performance of the parameter estimates under the Cox proportional hazards model by treating discrete survival times as continuous survival times, and the model comparison criteria, AIC and BIC, in determining links and baseline hazards. A SEER breast cancer dataset is analyzed in details to further demonstrate the proposed methodology.

Suggested Citation

  • Hee-Koung Joeng & Ming-Hui Chen & Sangwook Kang, 2016. "Proportional exponentiated link transformed hazards (ELTH) models for discrete time survival data with application," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 38-62, January.
    • Handle: RePEc:spr:lifeda:v:22:y:2016:i:1:d:10.1007_s10985-015-9326-z
    DOI: 10.1007/s10985-015-9326-z
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    References listed on IDEAS

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    Cited by:

    1. Hee-Koung Joeng & Abidemi K. Adeniji & Naitee Ting & Ming-Hui Chen, 2022. "Estimation of Discrete Survival Function through Modeling Diagnostic Accuracy for Mismeasured Outcome Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(1), pages 105-138, April.

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