PARAMETRIC AND SEMI-PARAMETRIC SURVIVAL MODELS WITH APPLICATION TO HYPERTENSION DATA

Abstract

Correlated survival data with possible censoring are frequently encountered in survival analysis.When there are dependencies among observed survival times, conventional Cox proportionalhazards model (CPHM) and Accelerated Failure Time (AFT) models that assumes independentresponses may not be appropriate. In this study, we compare the performance of parametric andsemi-parametric survival models with application to clinical data. Specifically, the AFT modeland the CPHM with and without Random effect were compared. Data on hypertension wascollected from Federal Medical Centre Keffi and General Hospital Nasarawa for the period offive years (2016 – 2020). The results from the analysis revealed that the Weibull AFT modelwith Gamma Random effect distribution had the least AIC and BIC values indicating that itoutperformed the other models considered in this study. Hence, the interpretation of the resultswas based on the most efficient model. Based on our results, it was found that hypertensionpatient that were giving drugs on the visit to the hospital has longer survival time compared tothose that were not giving drugs. Also, Hypertension patient with blood group AB and Obesedhave lesser survival time as compared to those with blood group o+ and normal weightrespectively. The study recommend that health expert can use the Weibull AFT model withGamma Random effect for predicting the risk factors of Hypertension especially when the dataare correlated.Keywords: AFT, CPHM, Hazard, Hypertension, Survival,