title: Proportional exponentiated link transformed hazards (ELTH) models for discrete time survival data with application.
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.
authors.affiliation: Department of Statistics, University of Connecticut, Storrs, CT, 06269, USA.
authors.forename: Hee-Koung
authors.initials: HK
authors.lastname: Joeng
authors.affiliation: Department of Statistics, University of Connecticut, Storrs, CT, 06269, USA. ming-hui.chen@uconn.edu.
authors.forename: Ming-Hui
authors.initials: MH
authors.lastname: Chen
authors.affiliation: Department of Applied Statistics, Yonsei University, Seoul, 120-749, South Korea.
authors.forename: Sangwook
authors.initials: S
authors.lastname: Kang
mesh_terms: D001943:Breast Neoplasms / Q000401:mortality; D003198:Computer Simulation; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D016016:Proportional Hazards Models; D016019:Survival Analysis
keywords: -link; C-log-log link; Cure rate models; Logit; Maximum likelihood estimate (MLE); Probit; SEER breast cancer data