Research Article
Year 2018,
Volume: 11 Issue: 1-2, 1 - 11, 01.01.2018
Abstract
In this study, a modelling strategy is developed to obtain more information from censored obser-
vations. By the proposed approach, uncensored observations are clustered using a fuzzy c-means algorithm
and the degrees to which censored observations are members of these clusters are determined. Censored
observations are weighted based on their membership values and the distances between the censoring time
and the time components of the cluster centres. Further, simulation studies are performed to characterize
the performance of the proposed approach based on the explained risk measure.
Keywords
Censored data Explained risk Fuzzy c-means algorithm Proportional hazards regression models survival analysis
References
- 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.
Year 2018,
Volume: 11 Issue: 1-2, 1 - 11, 01.01.2018
Abstract
References
- 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.
There are 10 citations in total.
Details
| Primary Language | English |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | January 1, 2018 |
| Acceptance Date | January 20, 2018 |
| Published in Issue | Year 2018 Volume: 11 Issue: 1-2 |
