PARAMETRIC AND SEMI-PARAMETRIC SURVIVAL MODELS WITH APPLICATION TO HYPERTENSION DATA - GANAKA KUBI, MUSA; LASISI K. E.; ABDULKADR RASHEED, BELLO; USMAN, Y. B.; LAWAN, ABDULLAHI (BIMA JOURNAL OF SCIENCE AND TECHNOLOGY GOMBE, 2022)
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.