手法を比較

	リスク調整済みスクリーニング検査評価	リスク調整コホート研究
分野	疫学	疫学
系統	Process / pipeline	Process / pipeline
提唱年	Late 1990s–2000s (formal statistical framework ~1997–2009)	Mid–late 20th century (risk-adjusted cohort designs systematized by 1970s–1990s)
提唱者	Margaret Sullivan Pepe and colleagues (covariate-adjusted ROC methodology)	Evolution of cohort study methodology; risk adjustment formalized through work of Rothman, Greenland, and others in epidemiology, 20th century
種類	Analytical study design	Observational epidemiological study design with statistical confounding control
原典	Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press. ISBN: 978-0198565826	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641
別名	risk-stratified screening accuracy study, covariate-adjusted diagnostic accuracy evaluation, risk-adjusted screening performance assessment, RASTE	adjusted cohort study, covariate-adjusted cohort, risk-controlled prospective study, propensity-adjusted cohort
関連	6	4
概要	Risk-adjusted screening test evaluation assesses the sensitivity, specificity, and overall discriminatory accuracy of a screening test after accounting for patient-level risk factors (covariates) that independently influence test results or disease prevalence. By conditioning performance metrics on observed covariates — age, sex, comorbidities, or prior screening history — this approach yields accuracy estimates that are not confounded by differences in population risk profiles, enabling fair comparisons across subgroups or study settings.	A risk-adjusted cohort study is an observational epidemiological design in which a defined group of individuals is followed over time to compare outcomes between exposed and unexposed subgroups, with statistical methods applied to control for measured confounders. Adjustment strategies — including multivariable regression, propensity score matching, inverse probability weighting, or standardization — are used to reduce bias and produce effect estimates that more closely approximate what would be observed in a randomized trial.