Сравнение методов

	Вложенное исследование «случай-контроль» с поправкой на риск	Регрессионная модель пропорциональных рисков Кокса
Область	Эпидемиология	Эпидемиология
Семейство	Process / pipeline	Process / pipeline
Год появления	1977 (nested case-control); risk-adjusted extensions 1980s–2000s	1972
Автор метода	Thomas (1977) for nested case-control; risk adjustment extensions developed through pharmacoepidemiology literature (1980s–2000s)	Sir David Roxbee Cox
Тип	Observational analytical study design	Semi-parametric regression model
Основополагающий источник	Thomas, D. C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. Journal of the Royal Statistical Society, Series A, 140(4), 469–491.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
Другие названия	risk-adjusted NCC, covariate-adjusted nested case-control, propensity-score nested case-control, nested case-control with risk adjustment	Cox regression, Cox PH model, proportional hazards model, CPH
Связанные	4	5
Сводка	A risk-adjusted nested case-control study embeds a case-control comparison inside a defined cohort and explicitly accounts for differences in baseline risk between cases and controls through covariate adjustment — most commonly via risk scores, propensity scores, or stratification. It preserves the efficiency advantages of the nested design while reducing confounding attributable to pre-existing risk differentials, making it especially valuable in pharmacoepidemiology and clinical effectiveness research.	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.