A class of semiparametric transformation models for survival data with a cured proportion

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• 作者：Sangbum Choi (1)
  Xuelin Huang (1)
  Yi-Hau Chen (2)
  
• 关键词：Counting process ; Crossing survivals ; Discrete frailty ; Nonparametric likelihood ; Survival analysis ; Transformation model
• 刊名：Lifetime Data Analysis
• 出版年：2014
• 出版时间：July 2014
• 年：2014
• 卷：20
• 期：3
• 页码：369-386
• 全文大小：
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• 作者单位：Sangbum Choi (1)
  Xuelin Huang (1)
  Yi-Hau Chen (2)
  
  1. Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 1411, Houston, TX, 77030, USA
  2. Institute of Statistical Science, Academia Sinica, Taipei, 11529, Taiwan
  
• ISSN：1572-9249

文摘

We propose a new class of semiparametric regression models based on a multiplicative frailty assumption with a discrete frailty, which may account for cured subgroup in population. The cure model framework is then recast as a problem with a transformation model. The proposed models can explain a broad range of nonproportional hazards structures along with a cured proportion. An efficient and simple algorithm based on the martingale process is developed to locate the nonparametric maximum likelihood estimator. Unlike existing expectation-maximization based methods, our approach directly maximizes a nonparametric likelihood function, and the calculation of consistent variance estimates is immediate. The proposed method is useful for resolving identifiability features embedded in semiparametric cure models. Simulation studies are presented to demonstrate the finite sample properties of the proposed method. A case study of stage III soft-tissue sarcoma is given as an illustration.