Methoden vergleichen

	Pragmatische Nested Case-Control-Studie	Retrospektive Nischen-Fall-Kontroll-Studie
Fachgebiet	Epidemiologie	Epidemiologie
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1977 (nested case-control); pragmatic variant emerged in real-world evidence research from 1990s onwards	1973 (formal description); widely adopted in epidemiology from 1980s onward
Urheber	Duncan Thomas (nested case-control); pragmatic design concept from Schwartz & Lellouch (1967)	Nested case-control formalized by Mantel (1973); retrospective application via historical cohort records
Typ	Observational epidemiological study design	Observational analytic study design
Wegweisende Quelle	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.	Mantel, N. (1973). Synthetic retrospective studies and related topics. Biometrics, 29(3), 479–486.
Aliasnamen	real-world nested case-control, pragmatic NCC, nested case-control in routine data, real-world evidence nested case-control	retrospective NCC, nested case-control within retrospective cohort, case-control nested in historical cohort, nested CCR
Verwandt	5	5
Zusammenfassung	A pragmatic nested case-control study embeds a case-control analysis within a pre-existing real-world cohort — typically drawn from electronic health records, administrative claims, or disease registries — to examine associations between exposures and outcomes under routine clinical conditions. Controls are sampled from the risk set (those still at risk at the time each case occurs), preserving temporal sequence while dramatically reducing data-collection costs compared with a full cohort analysis.	A retrospective nested case-control study is an efficient observational design in which cases and matched controls are sampled from within an already-assembled retrospective cohort. Exposure data are retrieved from historical records only for selected participants, dramatically reducing data-collection costs while retaining most of the analytic power of the full cohort. It is widely used in pharmacoepidemiology, occupational health, and disease-registry research.