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Updated: Aug 27, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Comparing parametric and Cox regression models using HIV/AIDS survival data from a retrospective study in Ntcheu
Rabson Dzinza1, Alfred Ngwira2
1Mathematics and Statistics Department, Nalikule College of Education, Lilongwe, Malawi.
Background:
The study was designed to compare parametric and Cox regression survival models. It was also aimed at determining risk factors of death due to HIV/AIDS.
Design And Methods:
The models were fitted to time from ART initiation to death due to HIV/AIDS while using data that was collected from 6670 patients records who registered for ART from 2007 to 2012 at Ntcheu district hospital in Malawi. The best fitting model was used to determine risk factors of death due to HIV/AIDS.
Results:
The exponential and Gompertz model competed very well with the Cox regression. Patients in WHO clinical stage 4 (HR = 1.69, p-value <0.001) and males (HR = 1.74, p-value <0.001) were associated with increased hazard of death than those in WHO clinical stage 3 and females. Patients with high body mass index (HR = 0.82, p-value <0.001) were associated with lower hazard of death than those with lower body mass index.
Conclusions:
Parametric models may perform as good as the Cox regression and the plausibility of all models needs to be investigated to use the correct model for accurate inferences. Furthermore, strategies to limit deaths due to HIV/AIDS should initiate ART early before WHO clinical stage 4 and males should receive special attention. The strategies should also aim at improving the body mass index of patients.
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