পদ্ধতির তুলনা করুন

	Clustered Survival Data-র জন্য Shared Frailty Model	Longitudinal and Time-to-Event Data-র জন্য Joint Model	Kaplan-Meier Survival Estimator	পুনরাবৃত্ত ঘটনা উত্তরন মডেল
ক্ষেত্র	উত্তরজীবিতা	উত্তরজীবিতা	উত্তরজীবিতা	উত্তরজীবিতা
পরিবার	Survival analysis	Survival analysis	Survival analysis	Survival analysis
উদ্ভবের বছর	1979	2004	1958	1981
প্রবর্তক	Vaupel, J.W., Manton, K.G. & Stallard, E.	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.	Kaplan, E. L. & Meier, P.	Andersen & Gill (AG, 1982); Prentice, Williams & Peterson (PWP, 1981); Wei, Lin & Weissfeld (WLW, 1989)
ধরন	Random effects survival model	Semiparametric regression model	Non-parametric survival estimator	Semi-parametric hazard model for repeated events
মৌলিক উৎস	Vaupel, J.W., Manton, K.G. & Stallard, E. (1979). The Impact of Heterogeneity in Individual Frailty on the Dynamics of Mortality. Demography, 16(3), 439–454.	Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press.	Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.	Cook, R.J. & Lawless, J.F. (2007). The Statistical Analysis of Recurrent Events. Springer.
অপর নাম	shared frailty model, random effects survival model, Frailty Modeli (Paylaşılan Kırılganlık)	joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model)	product-limit estimator, km curve, kaplan-meier sağkalım analizi	Tekrarlayan Olay Modeli (Recurrent Events), Andersen-Gill model, AG model, Wei-Lin-Weissfeld model
সম্পর্কিত	3	5	2	4
সারসংক্ষেপ	The shared frailty model, introduced by Vaupel, Manton, and Stallard in 1979, extends standard survival regression by incorporating a random effect — the 'frailty' — that captures unobserved heterogeneity among subjects or clusters. When survival outcomes are measured on individuals who share a common environment (patients in the same hospital, members of the same family, animals in the same litter), a frailty term accounts for the within-cluster dependence that ordinary Cox regression ignores.	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.	The Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups.	A recurrent event model is a survival analysis extension, formalised through the landmark contributions of Prentice, Williams and Peterson (1981), Andersen and Gill (1982), and Wei, Lin and Weissfeld (1989), that models time-to-event data when the same event — such as a hospital readmission, disease relapse, or equipment failure — can occur multiple times in the same individual. The three principal frameworks are the Andersen-Gill (AG) model, the Prentice-Williams-Peterson (PWP) stratified model, and the Wei-Lin-Weissfeld (WLW) marginal model, each making different assumptions about within-subject dependence.