A MODELLING APPROACH TO INCREASE THE EXPLAINED RISK IN THE PROPORTIONAL HAZARDS REGRESSION
A MODELLING APPROACH TO INCREASE THE EXPLAINED RISK IN THE PROPORTIONAL HAZARDS REGRESSION
In this study, a modelling strategy is developed to obtain more information from censored obser-
___
- Bezdek, J.C. (1971). Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York.
- Bezdek, J.C., Ehrlich R. and Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences, 10(1984), 191 203.
- Choodari-Oskooei, B., Royston P. and Parmar, M. (2012). A simulation study of predictive ability measures in a survival model 1: explained variation measures. Statistics in Medicine, 31, 2627-2643.
- Cox, D. (1972). Regression models and life-tables. Journal of the Royal Statistical Society B, 34, 187-220.
- Dunn, J.C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated cluster. Journal of Cybernetics, 3, 32-57.
- Goldberg, R.J., Gore, J.M., Alpert J.S. and Dalen, J.E. (1986). Recent changes in attack and survival rates of acute myocardial infarction (1975 through 1981): the Worcester heart attack study. Journal of the American Medical Association, 255, 2774-2779.
- Heller, G. (2012). A measure of explained risk in the proportional hazards model. Biostatistics, 13, 315-325.
- Hosmer Jr., D.W., Lemeshow, S. and May, S., 2008. Applied Survival Analysis: Regression Modeling of Time-to-Event Data, Wiley, Hoboken.
- Kent, J.T. (1983). Information gain and a general measure of correlation. Biometrika, 70, 163-173.
- Kleinbaum, D. and Klein, M. (2005). Survival Analysis: A Self-Learning Text, Springer, New York.