Advanced Joint Survival Models for Evaluating Treatment Efficacy

Abdulmuttaleb, Thaer

doi:10.25673/123111

Proceedings of International Conference on Applied Innovation in IT · 2025/12/22 · Vol. 13 · Issue 5 · pp. 817–826

Advanced Joint Survival Models for Evaluating Treatment Efficacy

Thaer Hashim Abdulmuttaleb

📄 Download PDF DOI: 10.25673/123111

Abstract

This study proposes a joint modelling framework for evaluating treatment efficacy in AIDS by integrating longitudinal and survival data. The approach addresses a key limitation in biomedical analysis by incorporating left-censored covariates into a unified likelihood function, ensuring more accurate and unbiased parameter estimation. A linear mixed-effects model is used to describe longitudinal biomarker dynamics, while a Weibull frailty model captures time-to-event outcomes and unobserved heterogeneity. The performance of the proposed model is assessed through Monte Carlo simulations under varying sample sizes and hazard conditions. Results indicate improved estimation accuracy, reduced bias, and greater robustness compared to conventional methods, particularly in the presence of censored or incomplete data. In addition, the framework effectively captures the association between longitudinal processes and survival outcomes, offering deeper insight into disease progression. Overall, the proposed method provides a reliable and flexible tool for clinical data analysis. It supports more informed decision-making in treatment evaluation and has potential applications in AIDS research and other chronic disease studies.

Keywords

AIDS Management Biomedical Applications Linear Mixed-Effects Model Longitudinal Data Monte Carlo Simulations Treatment Efficacy Weibull Frailty Model.

References

P. D. Allison, Survival Analysis Using SAS: A Practical Guide, SAS Institute, 2010.
E. DeJesús et al., “Pharmacogenomics in HIV treatment: Optimizing antiretroviral therapy through genetic profiling,” Journal of HIV and Pharmacogenomics, vol. 10, no. 3, pp. 245-259, 2022.
Department of Health and Human Services (DHHS), AIDS Clinical Guidelines, U.S. Government Printing Office, 2023.
P. Hougaard, Analysis of Survival Data, Springer, 2000.
E. L. Kaplan and P. Meier, “Nonparametric estimation from incomplete observations,” Journal of the American Statistical Association, vol. 53, no. 282, pp. 457-481, 1958.
J. D. Kalbfleisch and R. L. Prentice, The Statistical Analysis of Failure Time Data, 2nd ed., Wiley-Interscience, 2002.
J. Kazempoor and A. Habibirad, “Component-based perfect replacement optimizations for series and parallel systems consisting of repairable dependent components,” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, vol. 238, no. 6, pp. 1287-1301, 2024.
J. Kazempoor, A. Habibirad, and K. Okhli, “Bounds for CDFs of order statistics arising from INID random variables,” Journal of the Iranian Statistical Society, vol. 19, no. 1, pp. 39-57, 2022.
J. Kazempoor, A. Habibirad, and S. Sinha, “Joint occurrences of competing risks and multivariate longitudinal data: A prediction investigation for the HIV long data,” Revista Colombiana de Estadística, vol. 47, no. 2, p. 385, 2024.
J. P. Klein and M. L. Moeschberger, Survival Analysis: Techniques for Censored and Truncated Data, 2nd ed., Springer, 2003.
S. M. Mirjalili and J. Kazempoor, “Life extension for a coherent system through cold standby and minimal repair policies for their independent components,” International Journal of Reliability, Risk and Safety: Theory and Application, vol. 3, no. 2, pp. 51-54, 2020.
R. Soni and J. Smith, “Real-world evidence in clinical decision making,” The Lancet HIV, vol. 8, no. 2, pp. 102-108, 2021.
J. P. Sy and O. O. Aalen, “A cure model for cancer survival data,” Biometrics, vol. 57, no. 1, pp. 22-29, 2001.
T. M. Therneau and P. M. Grambsch, Modeling Survival Data: Extending the Cox Model, Springer, 2000.
J. X. Pan and K. T. Fang, Maximum Likelihood Estimation: Growth Curve Models and Statistical Diagnostics, pp. 77-158, 2002.
I. J. Myung, “Tutorial on maximum likelihood estimation,” Journal of Mathematical Psychology, vol. 47, no. 1, pp. 90-100, 2003.
A. Sattar and S. K. Sinha, “Joint modeling of longitudinal and survival data with a covariate subject to a limit of detection,” Statistical Methods in Medical Research, vol. 0, no. 0, pp. 1-17, 2017.
M. G. Hudgens, C. Li, and J. P. Fine, “Parametric likelihood inference for interval censored competing risks data,” Biometrics, vol. 70, no. 1, pp. 1-9, 2014.