Joint Modelling of Longitudinal and Survival Data for Breast Cancer Patients in Dharmais Cancer Hospital Indonesia.

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Bibliographic Details
Title:	Joint Modelling of Longitudinal and Survival Data for Breast Cancer Patients in Dharmais Cancer Hospital Indonesia.
Authors:	Ferdias, Pandri¹ pandriferdias@mail.ugm.ac.id, Gunardi² gunardi@ugm.ac.id, Danardono² danardono@ugm.ac.id, Ramadhan³ ramadhan@dharmais-surgonc.com
Source:	IAENG International Journal of Applied Mathematics. May2026, Vol. 56 Issue 5, p1790-1797. 8p.
Subjects:	Survival analysis (Biometry), Biomarkers, Prognosis, Indonesians, Disease progression, Breast tumors, Statistical models, Longitudinal method
Geographic Terms:	Indonesia
Abstract:	Breast cancer remains a major contributor to female mortality in Indonesia. In this study, an integrated joint modeling approach combining longitudinal biomarker analysis and survival outcomes is employed to evaluate the prognostic relevance of CA 15-3 levels together with important clinical factors among patients treated at Dharmais Cancer Hospital. The longitudinal trajectory of CA 15-3 was characterized using an enhanced transformation-based mixed-effects approach, while survival analysis was conducted through Cox proportional hazards models with various interaction structures. Among the four joint model specifications evaluated, cancer stage consistently emerged as the most robust predictor of mortality (hazard ratio ≈4.3-4.5, p < 0.005). Models integrating an interaction between CA 15-3 and age demonstrated superior performance, achieving the highest concordance index (0.72) and the lowest Akaike Information Criterion (AIC), although the interaction term itself did not reach statistical significance. Furthermore, treatment type exerted no significant influence on survival, and predictions derived from the mixed-effects longitudinal model exhibited numerical instability within the survival component. These findings underscore that disease stage remains the dominant determinant of mortality, while the prognostic utility of CA 15-3 is context-dependent, providing maximal information when integrated with patient-specific factors such as age. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Breast cancer remains a major contributor to female mortality in Indonesia. In this study, an integrated joint modeling approach combining longitudinal biomarker analysis and survival outcomes is employed to evaluate the prognostic relevance of CA 15-3 levels together with important clinical factors among patients treated at Dharmais Cancer Hospital. The longitudinal trajectory of CA 15-3 was characterized using an enhanced transformation-based mixed-effects approach, while survival analysis was conducted through Cox proportional hazards models with various interaction structures. Among the four joint model specifications evaluated, cancer stage consistently emerged as the most robust predictor of mortality (hazard ratio ≈4.3-4.5, p < 0.005). Models integrating an interaction between CA 15-3 and age demonstrated superior performance, achieving the highest concordance index (0.72) and the lowest Akaike Information Criterion (AIC), although the interaction term itself did not reach statistical significance. Furthermore, treatment type exerted no significant influence on survival, and predictions derived from the mixed-effects longitudinal model exhibited numerical instability within the survival component. These findings underscore that disease stage remains the dominant determinant of mortality, while the prognostic utility of CA 15-3 is context-dependent, providing maximal information when integrated with patient-specific factors such as age. [ABSTRACT FROM AUTHOR]
ISSN:	19929978