Extensions of cox model for non-proportional hazards purpose
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Cox proportional hazard model is one of the most common methods used in time to event analysis. The idea of the model is to define a hazard level as a dependent variable which is explained by the time-related component (so-called baseline hazard) and the covariates- related component. The model is based on several restrictive assumptions one of which is the assumption of proportional hazard. However, if this assumption is violated, this does not necessarily prevent an analyst from using Cox model. The current paper presents two ways of model modification in the case of non-proportional hazards: introducing interactions of selected covariates with function of time and stratification model. Calculations performed give the evidence that both methods result in better model fit as compared with the original model. Additionally, they allow interpreting the parameters estimates more precisely, taking into account the effect of the covariate at the hazard level that is changing over time. The choice of the appropriate method of tied events handling however is not straightforward and should be adjusted to the particular analysis purpose.
- Allison P.D., 1995, Survival Analysis Using SAS®. A Practical Guide, SAS Institute Inc., Cary NC.
- Cox D.R., 1972, Regression models and life-tables, Journal of the Royal Statistical Society. Series B (Methodological), vol. 34, no. 2, pp. 187–220.
- Hosmer D., Lemeshow S., 1999, Applied Survival Analysis. Regression Modeling Time to Event Data, John Wiley & Sons Inc., New York.
- Kalbfleisch J.D., Prentice R.L., 1980, The Statistical Analysis of Failure Time Data, John Wiley & Sons Inc., New Jersey.
- Quantin C, Moreau, Т., Asselain В., Maccario J., Lellouch, J., 1996, A regression survival model for testing the proportional hazards hypothesis, Biometrics, no. 52, pp. 874–885.
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