Salīdzināt metodes

	Meta-analītiskā izdzīvošanas analīze	Koksa proporcionālo risku modelis
Nozare	Epidemioloģija	Epidemioloģija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1990s–2000s (formalized ~1998)	1972
Autors	Parmar, Torri & Stewart (statistical framework); broader IPD tradition developed by the Early Breast Cancer Trialists' Collaborative Group	Sir David Roxbee Cox
Tips	Quantitative synthesis / meta-analytic method	Semi-parametric regression model
Pirmavots	Parmar, M. K. B., Torri, V., & Stewart, L. (1998). Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Statistics in Medicine, 17(24), 2815–2834.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
Citi nosaukumi	meta-analysis of time-to-event data, pooled survival analysis, IPD survival meta-analysis, aggregate survival meta-analysis	Cox regression, Cox PH model, proportional hazards model, CPH
Saistītās	4	5
Kopsavilkums	Meta-analytic survival analysis is a quantitative synthesis method that pools hazard ratios and related time-to-event statistics from multiple independent studies to produce a single, more precise estimate of a treatment or exposure effect on survival outcomes such as overall survival, disease-free survival, or time to relapse. It can operate on aggregate published data or on individual patient data (IPD) contributed directly by study investigators.	The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research.