Flexible Parametric Survival Model for Analysing Censored Time-To-Event Data Among Tuberculosis Patients.

Section: Research Paper
Issue
Vol. 21 No. 2 (2024): Volume 21 Issue 2
Published
Jun 25, 2025
Pages
165-180

Abstract

Tuberculosis (TB) is a globally deadly infectious disease responsible for 10 million new cases and 1.5 million deaths annually. Shorter TB treatment regimens show promise in reducing this problem, but there is an improved treatment success rate in South Africa, while retreatment cases remain a concern. An important feature of time-to-event modelling is its ability to consider transition probabilities of heterogeneous subgroups with different risk profiles. Survival analysis is generally performed to accurately estimate the transition probabilities associated with the risk profiles. This study explored the application of a flexible parametric survival model for analysing censored time-to-event data among TB patients.The data were obtained from East London Central Clinic-TB unit, Eastern Cape, South Africa. In total, 174 patients were included in the analysis. The goodness of fit of the models was explored using Akaike information criterion (AIC). We estimated the hazard ratios (HR) and baseline cumulative hazards of our model, which are necessary to calculate individual transition probabilities, and compared the model with the Cox model and additive hazard model to determine the survival predictions of TB patients.The flexible parametric survival model produced hazard ratio and baseline cumulative hazard estimates that were similar to those obtained using the Cox proportional hazards model. The analysis revealed that sex (HR=0.49, 95% CI: 0.38, 0.62), antiretroviral therapy (ART), (HR=0.53, 95% CI: 0.34, 0.78), and diabetes (HR=0.58, 95% CI: 0.41, 0.78) were all statistically significant factors associated with improved treatment survival in tuberculosis patients.Flexible parametric survival models are a powerful tool for modelling time-to-event data and individual transition probabilities. It is of great importance to fit models by modelling the baseline, which makes it easier to make different types of predictions and allows for non-proportional hazards since it is an interaction.

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Authors

Azeez A Adeboye

Department of Statistics, University of Fort Hare, Alice, Eastern Cape, South Africa,

ORCID
Osuji G Geogeleen

Department of Statistics, University of Fort Hare, Alice, Eastern Cape, South Africa

Alakija A Temitope

Department of Statistics, Yaba College of Technology, Yaba, Lagos State, Nigeria,

ORCID
Odeyemi O Akinwumi

2Department of Statistics, Yaba College of Technology, Yaba, Lagos State, Nigeria,

Mutambayi Ruffin

Department of Mathematical Sciences, Olabisi Onabanjo University, Ago-Iwoye, Ogun State, Nigeria.

Peter N. Madu

Olabisi Onabanjo University, Ago-Iwoye, Department of Mathematical Sciences , Nigeria

ORCID

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How to Cite

A Adeboye, A., عزيز, G Geogeleen, O., أوسوجي, A Temitope, A., أوديمي, O Akinwumi, O., أوديمي, Ruffin, M., موتامباي, N. Madu, P., & مادو. (2025). Flexible Parametric Survival Model for Analysing Censored Time-To-Event Data Among Tuberculosis Patients. IRAQI JOURNAL OF STATISTICAL SCIENCES, 21(2), 165–180. Retrieved from https://rjps.uomosul.edu.iq/index.php/stats/article/view/21004