Data generation algorithm.

<div><p>In survival analysis, it is often assumed that all individuals will eventually experience the event of interest if followed long enough. However, in many real-world scenarios, a subset of individuals remains event-free indefinitely. For instance, in clinical studies, some patient...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Aluwani Ramalata (21262977) (author)
مؤلفون آخرون:	Akim Adekpedjou (18933560) (author), Maseka Lesaoana (20008329) (author)
منشور في:	2025
الموضوعات:	Biotechnology Ecology Immunology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified followed long enough selecting relevant covariates patients never relapse traditional survival models mixture cure modeling improve model interpretability facilitate variable selection div >< p considered cured rather accommodate scad regularization individuals remains event variable selection varying covariates 19 patients world scenarios world data varying effects susceptible individuals survival analysis study develops south africa proposed approach practical applicability penalized logistic often assumed limpopo province latency components key challenge implemented using free indefinitely eventually experience clinical studies
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Data generation algorithm.

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