Estimation of the Joint Model for High-Dimensional Longitudinal Survival Data Using the Hybrid Genetic Algorithm–EM Method (HGA-EM)
DOI:
https://doi.org/10.31272/jae.i151.1537Keywords:
Joint Model, Survival Hazard, Survival Data, Longitudinal DataAbstract
Joint models are advanced ststistical methods that allow the integrated analysis of longitudinal data and survival data, particularly when there is a structural association between the temporal trajectory of biomarker and the risk of a clinical event .
In this context, the present study aims to develop a more efficient estimation of joint model parameters by adopting the Hybrid Genetic Algorithm-Expectation Maximization (HGA_GA)approach ,which combines the global search capability of genetic algorithms with the optimization and stabilization properties of the EM algorithm ,This thalassemia , including three longitudinal biomarkers (HB,MCV and Urea ) measured over three follow -up visits . The results showed that the HB index exhibited a gradual increase over time (0.0551),and higher HB values were associated with anoticeable reduction in the risk of relapse , as indicated by anegative longitudinal - survival association parameter (-0.0823) the mean MCV was approximately 77.99 with alimited time effect (0.1205) , and it demonstrated a slight risk-reducing survival association (-0.0088) in cntrast , urea was the most variable biomarker , increasing over time (0.2835) and showing a clearer association with increased probability of the event reflected by a relatively larger negative survival cofficient (-0.02745) overall these findings higlight the efficiency of the HGA_EM hybrid approach in achieving more stzble and accurate estimation than conventional methods and in precisely capturing the dynamic relationship between longitudinal biomarkres and the risk of clinical events.
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