A step-up procedure for selecting variables associated with survival.

Abstract

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.