Measures of explained variation for a regression model used in survival analysis

This paper describes a measure of explained variation (MEV) of survival times for a given regression model used in survival analysis. It quantifies the predictive power of a set of prognostic factors in the model, and therefore provides useful information for more precise prediction of patient prognosis, and for designing randomized clinical trials with the capability of determining treatment effects. The MEV defined in this article is asymptotically derived from the squared product-moment correlation; it can be interpreted as an adaptation of the multiple correlation coefficient for the normal linear model to the survival time regression model. Monte-Carlo simulations are performed to investigate the statistical behavior of the proposed MEV. The MEV is applied to estimate the predictive power of several sets of prognostic factors for gastric cancer in Japan using data from a large clinical trial.