Semiparametric Efficient Estimation for a Class of Generalized Proportional Odds Cure Models

• 成果类型: Article
• 署名作者: Mao, Meng; Wang, Jane-Ling
• 署名单位: University of California System; University of California Davis
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.tm08459
• 发表日期: 2010
• 页码: 302-311
• 关键词: maximum-likelihood-estimation; Mixture Model; regression

摘要

We present a mixture cure model with the survival time of the uncured group coming from a class of linear transformation models, which is an extension of the proportional odds model This class of model. first proposed by Dabrowska and Doksum ( 988). which we term generalized proportional odds model, is well suited for the mixture cure model setting due to a clear separation between long-term and short-term effects A standard expectation-maximization algorithm can he employed to locate the nonparametric likelihood estimators which are shown to he consistent and semiparametric efficient However. there are difficulties in the M-step due to the nonparametric component We overcome these difficulties by proposing two different algorithms The first is to employ an majorize-minimize (MM) algorithm in the M-step instead of the usual Newton-Raphson method, and the other is based on an alternative form to express the model as a proportional hazards frailty model The two new algorithms are compared in a simulation study with an existing estimating equation approach by DJ and Ying (2004) The MM algorithm provides both computational stability and efficiency A case study of leukemia dam is conducted to illustrate the proposed procedures