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 proportional
hazards model (CPHM) and Accelerated Failure Time (AFT) models that assumes independent
responses may not be appropriate. In this study, we compare the performance of parametric and
semi-parametric survival models with application to clinical data. Specifically, the AFT model
and the CPHM with and without Random effect were compared. Data on hypertension was
collected from Federal Medical Centre Keffi and General Hospital Nasarawa for the period of
five years (2016 – 2020). The results from the analysis revealed that the Weibull AFT model
with Gamma Random effect distribution had the least AIC and BIC values indicating that it
outperformed the other models considered in this study. Hence, the interpretation of the results
was based on the most efficient model. Based on our results, it was found that hypertension
patient that were giving drugs on the visit to the hospital has longer survival time compared to
those that were not giving drugs. Also, Hypertension patient with blood group AB and Obesed
have lesser survival time as compared to those with blood group o+ and normal weight
respectively. The study recommend that health expert can use the Weibull AFT model with
Gamma Random effect for predicting the risk factors of Hypertension especially when the data
are correlated.