পদ্ধতির তুলনা করুন

	Cox Regression with Time-Varying Covariates	Clustered Survival Data-র জন্য Shared Frailty Model
ক্ষেত্র	উত্তরজীবিতা	উত্তরজীবিতা
পরিবার	Survival analysis	Survival analysis
উদ্ভবের বছর	1972	1979
প্রবর্তক	Cox, D. R. (extended formulation by Therneau & Grambsch)	Vaupel, J.W., Manton, K.G. & Stallard, E.
ধরন	Semi-parametric hazard regression model	Random effects survival model
মৌলিক উৎস	Therneau, T. M. & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.	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.
অপর নাম	time-varying covariate Cox model, extended Cox model, Zamana Bağlı Kovaryatlı Cox Regresyonu	shared frailty model, random effects survival model, Frailty Modeli (Paylaşılan Kırılganlık)
সম্পর্কিত	4	3
সারসংক্ষেপ	Time-dependent Cox regression is an extension of the standard Cox proportional hazards model, introduced through the counting-process formulation developed by Therneau and Grambsch (2000), that allows one or more predictor variables to take different values at different points in a subject's follow-up period. It is the method of choice whenever a covariate — such as a laboratory measurement, a medication dose, or a disease severity score — changes over time rather than remaining fixed from study entry.	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.