Corbière Fabien, Joly Pierre
EMI E0338 Biostatistique, Institut de Santé Publique et Développement, Université Bordeaux 2, 146 rue Léo Saignat, 33076 Bordeaux Cedex, France.
Comput Methods Programs Biomed. 2007 Feb;85(2):173-80. doi: 10.1016/j.cmpb.2006.10.008. Epub 2006 Dec 8.
Cure models have been developed to analyze failure time data with a cured fraction. For such data, standard survival models are usually not appropriate because they do not account for the possibility of cure. Mixture cure models assume that the studied population is a mixture of susceptible individuals, who may experience the event of interest, and non-susceptible individuals that will never experience it. The aim of this paper is to propose a SAS macro to estimate parametric and semiparametric mixture cure models with covariates. The cure fraction can be modelled by various binary regression models. Parametric and semiparametric models can be used to model the survival of uncured individuals. The maximization of the likelihood function is performed using SAS PROC NLMIXED for parametric models and through an EM algorithm for the Cox's proportional hazards mixture cure model. Indications and limitations of the proposed macro are discussed and an example in the field of cancer clinical trials is shown.
已开发出治愈模型来分析带有治愈比例的失效时间数据。对于此类数据，标准生存模型通常并不适用，因为它们没有考虑到治愈的可能性。混合治愈模型假定所研究的总体是由可能经历感兴趣事件的易感个体和永远不会经历该事件的非易感个体组成的混合体。本文的目的是提出一个SAS宏程序，用于估计带有协变量的参数化和半参数化混合治愈模型。治愈比例可以通过各种二元回归模型进行建模。参数化和半参数化模型可用于对未治愈个体的生存情况进行建模。对于参数化模型，使用SAS PROC NLMIXED执行似然函数最大化，对于Cox比例风险混合治愈模型，则通过期望最大化（EM）算法来执行。讨论了所提出宏程序的适用情况和局限性，并给出了癌症临床试验领域的一个示例。
