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- Title
Kesirli Polinomlar ile Cox Regresyon Modeli: Prostat Kanseri Veri Kümesi Üzerine Bir Uygulama.
- Authors
DİNÇ, Hazal; ATA TUTKUN, Nihal
- Abstract
Objective: The use of regression models with fractional polynomial was proposed to model nonlinear effects of covariates. The inclusion of covariates into the model as continuous or categorical can change the results. However, converting continuous covariates into categorical format causes a loss of information. In this case, models with fractional polynomial become an appropriate alternative. The aim of this study is examining the use of fractional polynomials in Cox regression model. Material and Methods: Classical Cox regression model and Cox regression model with fractional polynomial were applied to the data set of 475 patients (Byar and Green, 1980) who were in the 3rd and 4th stages of prostate cancer disease and followed for 5 years.1 Results: Classical Cox regression analysis was performed by including type of treatment and stage of disease as categorical and age as continuous and it was found that age and stage of disease were statistically significant, but the assumption of proportional hazards was violated. When age was taken as a categorical covariate, the 81 and more level of age and the stage of disease was significant. However, the use of classical Cox regression model is not correct since the proportional hazards assumption was violated. When the Cox regression model with fractional polynomials was run for prostate cancer data set, it was found that the age variable had a nonlinear relationship with the survival time. Age and stage of disease were statistically significant and the assumption of proportional hazards was provided. Conclusion: It was concluded that the Cox regression model with fractional polynomials is more suitable for the prostate cancer dataset.
- Publication
Turkiye Klinikleri Journal of Biostatistics, 2020, Vol 12, Issue 1, p38
- ISSN
1308-7894
- Publication type
Academic Journal
- DOI
10.5336/biostatic.2019-71879