title: Measures of explained variation for survival data.
abstract: The predictive power of a set of prognostic variables in a survival time model is a concept distinct from the statistical significance of the variables or the adequacy of the model fit. In this paper we discuss the importance of quantifying the predictive power of a prognostic model, and suggest measures of explained variation as a possible quantification. The important features of our approach are that (1) the measures are completely model-based; (2) a specification of the time range of interest is easily incorporated; and (3) the null models used for comparison are derived as mixtures of the predicted distributions.
authors.affiliation: Department of Biomathematics, UCLA School of Medicine 90024.
authors.forename: E L
authors.initials: EL
authors.lastname: Korn
authors.forename: R
authors.initials: R
authors.lastname: Simon
mesh_terms: D000704:Analysis of Variance; D006801:Humans; D015233:Models, Statistical; D011237:Predictive Value of Tests; D011379:Prognosis; D011897:Random Allocation; D015203:Reproducibility of Results; D012307:Risk Factors; D015996:Survival Rate