Efficiency of a New Parametric Cox Proportional Hazard Model Using Monte Carlo Simulation Study
Abstract
In clinical studies, statistical models have proved useful in inferential analysis. This study examines the parametric Cox PH model using simulation studies. Through simulation studies, the study demonstrated the usefulness of the parametric Cox model by comparing its statistics, such as the concordance, confidence interval, redundancy or otherwise of the covariates, and median survival time, with those of classical Cox and logistic models. By extension, the ROC was plotted to show similarity with the parametric Cox model. The results show that the parametric Cox PH model has a higher concordance ratio of 0.9810 while the classical Cox PH model has 0.7810 concordance ratio. In both model scenarios, the variable Disease-Free survival indicator did not produce any value. The mean square error of the parametric Cox PH is lower than that of the classical Cox PH model. More covariates are significant in the parametric Cox PH model than in the classical Cox PH model. This tells that the proposed parametric Cox PH model improves the classical Cox PH model. The confidence interval for both models is seemingly the same. Because the assumptions of the Cox PH model were not violated in this study, given that the exponential distribution has a constant hazard rate, it is therefore recommended that other choices of non-constant hazard rate functions be made and deployed in the classical Cox PH model to attain some variant parametric Cox PH models.
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