Semiparametric transformation models for survival data with a cure fraction

• 成果类型: Article
• 署名作者: Zeng, Donglin; Yin, Guosheng; Ibrahim, Joseph G.
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of Texas System; UTMD Anderson Cancer Center
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214505000001122
• 发表日期: 2006
• 页码: 670-684
• 关键词: PROPORTIONAL HAZARDS MODEL; ASYMPTOTIC THEORY; regression; Consistency; inference

摘要

We propose a class of transformation models for survival data with a cure fraction. The class of transformation models is motivated by biological considerations and includes both the proportional hazards and the proportional odds cure models as two special cases. An efficient recursive algorithm is proposed to calculate the maximum likelihood estimators (MLEs). Furthermore, the MLEs for the regression coefficients are shown to be consistent and asymptotically normal, and their asymptotic variances attain the semiparametric efficiency bound. Simulation studies are conducted to examine the finite-sample properties of the proposed estimators. The method is illustrated on data from a clinical trial involving the treatment of melanoma.