Krall J M, Uthoff V A, Harley J B
Biometrics. 1975 Mar;31(1):49-57.
Multivariate concomitant information on a subject's condition usually accompanies survival time data. Using a model in which each subject's lifetime is exponentially distributed, this paper suggests a method which utilizes a step-up procedure for choosing the most important variables associated with survival. Maximum likelihood (ML) estimates are utilized, and the likelihood ratio is employed as the criterion for adding significant concomitant variables. An example using multiple myeloma survival data and sixteen concomitant variables is discussed in which three variables are chosen to predict survival.
关于受试者病情的多变量伴随信息通常与生存时间数据相伴出现。本文使用一种模型，其中每个受试者的寿命呈指数分布，提出了一种利用逐步法来选择与生存相关的最重要变量的方法。采用最大似然（ML）估计，并将似然比用作添加显著伴随变量的标准。文中讨论了一个使用多发性骨髓瘤生存数据和16个伴随变量的例子，从中选择了三个变量来预测生存情况。
