A comparison of multivariable mathematical methods for predicting survival--I. Introduction, rationale, and general strategy

Abstract

This paper and the two following papers (Parts I-III) report an investigation of performance variability for four multivariable methods: discriminant function analysis, and linear, logistic, and Cox regression. Each method was examined for its performance in using the same independent variables to develop predictive models for survival of a large cohort of patients with lung cancer. The cogent biologic attributes of the patients had previously been divided into five ordinal stages having a strong prognostic gradient. With stratified random sampling, we prepared seven "generating" sets of data in which the five biologic stages were arranged in proportional, uniform, symmetrical unimodal, decreasing exponential, increasing exponential, U-shaped, or bi-modal distributions. Each of the multivariable methods was applied to each of the seven generating distributions, and the results were tested in a separate "challenge" set, which had not been included in any of the generating sets. The research was intended not merely to compare the performance of the multivariable methods, but also to see how their performance would be affected by different statistical distributions of the same cogent biologic attributes. The results, which are presented in the second and third papers, were compared for selection of independent variables and coefficients, and for accuracy in fitting the generating sets and the challenge set.