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	Metaanalityczny projekt przypadek-krzyżowy	Badanie zagnieżdżone typu przypadek-kontrola
Dziedzina	Epidemiologia	Epidemiologia
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1991 (base design); meta-analytic applications from late 1990s onward	1973–1977
Twórca	Maclure (case-crossover basis, 1991); meta-analytic extension through environmental epidemiology consortia (1990s–2000s)	Nathan Mantel (1973); D. C. Thomas (1977 formalization)
Typ	Observational epidemiological design with meta-analytic synthesis	Hybrid observational study design
Źródło pierwotne	Maclure, M. (1991). The case-crossover design: a method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153.	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.
Inne nazwy	pooled case-crossover analysis, case-crossover meta-analysis, MACCO, systematic pooling of case-crossover studies	NCC study, nested CC design, case-control within cohort, density sampling case-control
Pokrewne	3	6
Podsumowanie	The meta-analytic case-crossover design combines the within-person control structure of the case-crossover study with formal meta-analytic pooling across multiple studies. Each contributing study uses cases as their own controls by comparing exposure windows immediately preceding an acute event to matched reference windows in the same individual. The pooled approach synthesizes conditional odds ratios across studies, maximizing statistical power and generalizability — commonly applied to short-term environmental exposures such as air pollution, temperature extremes, and drug triggers of acute events.	A nested case-control study is an efficient observational design embedded within a defined cohort. For each participant who develops the outcome of interest (a case), a small number of matched controls are sampled from those still at risk at the same point in time. This density-sampling strategy yields odds ratios that approximate incidence-rate ratios from the full cohort at a fraction of the data-collection cost — making it the preferred alternative when measuring exposures for all cohort members would be prohibitively expensive or technically demanding.