ADVANCED SURVIVAL MODELING OF TUBERCULOSIS PATIENTS: INSIGHTS FROM EXPONENTIAL AND WEIBULL AFT MODELS
Abstract
Tuberculosis is a significant public health issue in high-burden countries like Nigeria, causing increased disability and claiming many lives. The Cox Proportional Hazards model is commonly used in survival studies, but it fails to define the distribution of survival time. This study uses data from the National Tuberculosis and Leprosy Center (NTLC), Zaria, Kaduna State, Nigeria, to determine Tuberculosis survival and compare alternative parametric survival models. The objectives include determining predictors of TB mortality, evaluating the effect of these predictors on survival probability, and comparing Exponential and Weibull AFT models on NTLC Zaria TB survival data. The results show that the Weibull AFT model is most effective in modelling TB survival rates, with the lowest AIC score of 485.1 and the highest log likelihood of -228.6. Major factors associated with mortality include age over 55 years, pulmonary tuberculosis, family history of tuberculosis, alcohol and smoking history, and BMI less than 18.5 kgs/m2. The study emphasizes the need for region-specific survival models to reveal major directions for successful interventions and TB policies. Future studies should consider translating the highly-parametric approach into next-generation non-parametric models/machine learning for more accurate prognoses for implementing state-of-the-art public health interventions.
References
Ayala, A., Ncogo, P., Eyene, J., Garca, B., Benito, A., & Romay-Barja, M. (2023). Rural-Urban Inequities in Tuberculosis-Related Practices in Equatorial Guinea. Journal of Epidemiology and Global Health, 13(4), 886-894. https://doi.org/10.1007/s44197-023-00162-9 DOI: https://doi.org/10.1007/s44197-023-00162-9
Bajehson, M., Musa, B. M., Gidado, M., Nsa, B., Sani, U., Habibu, A. T., ... & Gida, Y. (2019). Determinants of mortality among patients with drug-resistant tuberculosis in northern Nigeria. PloS one, 14(11), e0225165. https://doi.org/10.1371/journal.pone.0225165 DOI: https://doi.org/10.1371/journal.pone.0225165
Collett, D. (2023). Modelling survival data in medical research. Chapman and Hall/CRC. https://doi.org/10.1201/9781003282525 DOI: https://doi.org/10.1201/9781003282525
Daniel, S., Lasisi, K. E., & Banister, J. (2020). Application of Survival Analysis of TB Patients Using Parametric Model: A Case Study of General Hospital Bayara. Asian Journal of Probability and Statistics, 6(4), 54-65. https://doi.org/10.9734/ajpas/2020/v6i430169 DOI: https://doi.org/10.9734/ajpas/2020/v6i430169
Habu, L., Usman, A., Sadiq, I. A., & Abdullahi, U. A. (2024). Estimation of Extension of Topp-Leone Distribution using Two Different Methods: Maximum Product Spacing and Maximum Likelihood Estimate. UMYU Scientifica, 3(2), 133-138. https://doi.org/10.56919/usci.2432.014 DOI: https://doi.org/10.56919/usci.2432.014
Jin, C., Wu, Y., Chen, J., Liu, J., Zhang, H., Qian, Q., & Pang, T. (2024). Prevalence and Patterns of Drug-Resistant Mycobacterium tuberculosis in Newly Diagnosed Patients in China: A Systematic Review and Meta-Analysis. Journal of Global Antimicrobial Resistance. https://doi.org/10.1016/j.jgar.2024.05.018 DOI: https://doi.org/10.1016/j.jgar.2024.05.018
Kleinbaum, D. G., & Klein, M. (2012). Survival Analysis: A Self-Learning Text. Springer Science & Business Media. DOI: https://doi.org/10.1007/978-1-4419-6646-9
Marzaman, A. N. F., Roska, T. P., Sartini, S., Utami, R. N., Sulistiawati, S., Enggi, C. K., ... & Permana, A. D. (2023). Recent advances in pharmaceutical approaches of antimicrobial agents for selective delivery in various administration routes. Antibiotics, 12(5), 822. https://doi.org/10.3390/antibiotics12050822 DOI: https://doi.org/10.3390/antibiotics12050822
Mumena, D. K. (2023). Genotypic Characterization of Drug-Resistant Mycobacterium Tuberculosis among the New and Previously Treated Tuberculosis Cases, from Health Facilities Across Seven Provinces of Zambia (Doctoral dissertation, JKUAT-COHES). http://localhost/xmlui/handle/123456789/6124
Obafemi, A. A., Usman, A., Sadiq, I. A., & Okon, U. (2024). A New Extension of Topp-Leone Distribution (NETD) Using Generalized Logarithmic Function. UMYU Scientifica, 3(4), 127-133. https://doi.org/10.56919/usci.2434.011 DOI: https://doi.org/10.56919/usci.2434.011
Ogunniyi, T. J., Abdulganiyu, M. O., Issa, J. B., Abdulhameed, I., & Batisani, K. (2024). Ending tuberculosis in Nigeria: a priority by 2030. BMJ Global Health, 9(12), e016820. https://doi.org/10.1136/bmjgh-2024-016820 DOI: https://doi.org/10.1136/bmjgh-2024-016820
Panitanarak, U., Ishaq, A. I., Singh, N. S. S., Usman, A., Usman, A. U., Daud, H., ... & Suleiman, A. A. (2025). Machine Learning Models in Predicting Failure Times Data Using a Novel Version of the Maxwell Model. European Journal of Statistics, 5, 1-1. https://doi.org/10.28924/ada/stat.5.1 DOI: https://doi.org/10.28924/ada/stat.5.1
Rexy, D. C., & Rayalu, G. M. (2024). Exploring Time-Dependent Factors in the Survival of Tuberculosis Patients: A Cox Proportional Hazards Analysis. Machine Intelligence Research, 18(1), 556-565. https://machineintelligenceresearchs.com/index.php/mir/article/view/50
Rikochi, C. L., Musa, A. Z., & Olowolafe, T. A. (2023). Implementation of WHO guideline on tuberculosis infection prevention and control in Kaduna State, Nigeria. PAMJ-One Health, 12(1). https://www.one-health.panafrican-med-journal.com/content/article/12/1/full DOI: https://doi.org/10.11604/pamj-oh.2023.12.1.41035
Sadiq, I. A., & Komali, K. (2020). On the Study of Clear Causal Risk Factors of Diabetes Mellitus Using Multiple Regression. Asian Journal of Probability and Statistics, 9(4), 29-47. https://doi.org/10.9734/AJPAS/2020/v9i430233
Sadiq, I. A., & Komali, K. (2020). On the Study of Clear Causal Risk Factors of Diabetes MellitusUsing Multiple Regression. Asian Journal of Probability and Statistics, 9(4), 29-47. DOI: https://doi.org/10.9734/ajpas/2020/v9i430233
Sadiq, I. A., Doguwa, S. I. S., Yahaya, A., & Garba, J. (2023c). New Generalized Odd Frchet-Odd Exponential-G Family of Distribution with Statistical Properties and Applications. FUDMA Journal of Sciences, 7(6), 41-51. https://doi.org/10.33003/fjs-2023-0706-2096 DOI: https://doi.org/10.33003/fjs-2023-0706-2096
Sadiq I. A., Garba, S., Kajuru, J. Y., Usman, A., Ishaq, A. I., Zakari, Y., Doguwa, S. I., & Yahaya, A. (2024). The Odd Rayleigh-G Family of Distribution: Properties, Applications, and Performance Comparisons. FUDMA JOURNAL OF SCIENCES, 8(6), 514 - 527. https://doi.org/10.33003/fjs-2024-0806-3011 DOI: https://doi.org/10.33003/fjs-2024-0806-3011
Sadiq, I. A., Doguwa, S. I. S., Yahaya, A., & Usman, A. (2023b). Development of New Generalized Odd Frchet-Exponentiated-G Family of Distribution. UMYU Scientifica, 2(4), 169-178. http://dx.doi.org/10.56919/usci.2324.021 DOI: https://doi.org/10.56919/usci.2324.021
Sadiq, I. A., Doguwa, S. I., Yahaya, A., & Garba, J. (2022). New Odd Frechet-G Family of Distribution with Statistical Properties and Applications. AFIT Journal of Science and Engineering Research, 2(2), 84-103.
Sadiq, I. A., Doguwa, S. I., Yahaya, A., & Garba, J. (2023a). New Generalized Odd Frechet-G (NGOF-G) Family of Distribution with Statistical Properties and Applications. UMYU Scientifica, 2(3), 100-107. http://dx.doi.org/10.56919/usci.2323.016 DOI: https://doi.org/10.56919/usci.2323.016
Sadiq, I. A., Raghav, J. S., & Sharma, S. K. (2020). Conglomeration of General Linear Model for Epilepsy Clinical Neuroimaging. Asian Journal of Probability and Statistics, 10(2), 1-12. https://doi.org/10.9734/AJPAS/2020/v10i230241 DOI: https://doi.org/10.9734/ajpas/2020/v10i230241
World Health Organization. (2024). Global Tuberculosis Report 2024. World Health Organization.
Usman , . A. ., Doguwa, S. I. ., Sadiq, I. A. ., & Akor, A. (2025). Exploring Accelerated Failure Time Models for Tuberculosis Survival: Loglogistic and Weibull Survival Regression Model. Journal of Science Research and Reviews, 2(1), 27-36. https://doi.org/10.70882/josrar.2025.v2i1.27 DOI: https://doi.org/10.70882/josrar.2025.v2i1.27
Yusuf, H., Usman, A., YahayaA., Sadiq, I. A., Bello, O. A., & Adamu, S. A. (2025). Modified Inverted Kumaraswamy Distribution Using Inverse Power Function: Properties And Applications. FUDMA JOURNAL OF SCIENCES, 9(1), 234 - 239. https://doi.org/10.33003/fjs-2025-0901-3177 DOI: https://doi.org/10.33003/fjs-2025-0901-3177
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