Vertaile menetelmiä

	Risk-adjusted diagnostic accuracy study	Logistinen regressio
Tieteenala	Epidemiologia	Tutkimuksen tilastomenetelmät
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	Conceptual roots 1980s–1990s; covariate-adjusted ROC formally introduced 2009	1958
Kehittäjä	Margaret Pepe and colleagues; covariate-adjusted ROC formalized by Janes & Pepe (2009)	David Roxbee Cox
Tyyppi	Observational clinical study design with covariate adjustment	Method
Alkuperäislähde	Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press. ISBN: 978-0198509844	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Rinnakkaisnimet	case-mix-adjusted diagnostic accuracy, stratified diagnostic accuracy study, covariate-adjusted diagnostic accuracy, risk-stratified DTA study	logit model, binomial logistic regression, LR
Liittyvät	6	3
Tiivistelmä	A risk-adjusted diagnostic accuracy study evaluates how well an index test identifies a target condition while explicitly accounting for patient-level risk factors that influence either disease prevalence or test performance. By adjusting for case-mix, it yields accuracy estimates — sensitivity, specificity, and AUC — that are not confounded by the composition of the study sample, enabling fairer comparisons across populations and clinical settings.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.