قارن الطرق

	النموذج المشترك للبيانات الطولية والبيانات المتعلقة بالوقت حتى وقوع حدث	نموذج التأثيرات المختلطة
المجال	تحليل البقاء	الإحصاء
العائلة	Survival analysis	Regression model
سنة النشأة	2004	1982
صاحب الطريقة	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.	Laird & Ware
النوع	Semiparametric regression model	Mixed effects regression
المصدر التأسيسي	Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press.	Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.
الأسماء البديلة	joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model)	LME, LMM, mixed model, random effects model
ذات صلة	5	4
الملخص	The joint model for longitudinal and time-to-event data, formalised by Tsiatis and Davidian in 2004 and extended comprehensively by Rizopoulos in 2012, simultaneously estimates a mixed-effects model for repeatedly measured biomarkers and a survival model for the time to an event, linking the two processes through shared random effects. It resolves two major problems that simpler approaches cannot handle: informative dropout from longitudinal studies and the endogeneity of time-varying biomarkers used as covariates in a Cox model.	A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated.