Linganisha mbinu

	Uchambuzi wa Nguvu kwa Ajili ya Tafiti za Usalama wa Maisha	Mfumo wa Cox Proportional Hazards
Nyanja	Takwimu	Epidemiolojia
Familia	Hypothesis test	Process / pipeline
Mwaka wa asili	1981	1972
Mwanzilishi	—	Sir David Roxbee Cox
Aina	Sample size determination for survival outcomes	Semi-parametric regression model
Chanzo asilia	Schoenfeld, D. A. (1981). The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika, 68(1), 316–319.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
Majina mbadala	log-rank power analysis, cox regression power analysis, survival power analysis, Sağkalım Analizi Güç Analizi	Cox regression, Cox PH model, proportional hazards model, CPH
Zinazohusiana	6	5
Muhtasari	Power analysis for survival studies determines how many participants — and how many observed events — are required so that a log-rank test or Cox regression has a sufficient probability of detecting a clinically meaningful difference in survival between groups. The foundational formulas were derived by Schoenfeld (1981) and Lachin (1981) and remain the standard approach in clinical trial planning.	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.