title: Measures of explained variation for a regression model used in survival analysis.
abstract: 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.
authors.affiliation: Kyushu University, Faculty of Medicine, Department of Medical Informatics, Fukuoka, Japan.
authors.forename: K
authors.initials: K
authors.lastname: Akazawa
mesh_terms: D000367:Age Factors; D000465:Algorithms; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use; D005544:Forecasting; D006801:Humans; D007564:Japan; D016014:Linear Models; D008197:Lymph Node Excision; D008207:Lymphatic Metastasis; D008954:Models, Biological; D009010:Monte Carlo Method; D009361:Neoplasm Invasiveness; D011379:Prognosis; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic; D012044:Regression Analysis; D012107:Research Design; D013274:Stomach Neoplasms / Q000145:classification / Q000188:drug therapy / Q000473:pathology / Q000601:surgery; D016019:Survival Analysis