半参数回归
计量经济学
统计
混合模型
协变量
半参数模型
估计员
数学
似然函数
审查(临床试验)
边际模型
计算机科学
回归分析
估计理论
作者
Sangbum Choi,Xuelin Huang
出处
期刊:Biometrics
[Wiley]
日期:2014-04-15
卷期号:70 (3): 588-598
被引量:12
摘要
Summary In the analysis of competing risks data, the cumulative incidence function is a useful quantity to characterize the crude risk of failure from a specific event type. In this article, we consider an efficient semiparametric analysis of mixture component models on cumulative incidence functions. Under the proposed mixture model, latency survival regressions given the event type are performed through a class of semiparametric models that encompasses the proportional hazards model and the proportional odds model, allowing for time‐dependent covariates. The marginal proportions of the occurrences of cause‐specific events are assessed by a multinomial logistic model. Our mixture modeling approach is advantageous in that it makes a joint estimation of model parameters associated with all competing risks under consideration, satisfying the constraint that the cumulative probability of failing from any cause adds up to one given any covariates. We develop a novel maximum likelihood scheme based on semiparametric regression analysis that facilitates efficient and reliable estimation. Statistical inferences can be conveniently made from the inverse of the observed information matrix. We establish the consistency and asymptotic normality of the proposed estimators. We validate small sample properties with simulations and demonstrate the methodology with a data set from a study of follicular lymphoma.
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