เปรียบเทียบวิธี

	การออกแบบเมตาอะนาไลติกแบบกรณี-ครอสโอเวอร์	การศึกษากลุ่มควบคุมที่ซ้อนกัน (Nested Case-Control Study)
สาขาวิชา	ระบาดวิทยา	ระบาดวิทยา
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1991 (base design); meta-analytic applications from late 1990s onward	1973–1977
ผู้ริเริ่ม	Maclure (case-crossover basis, 1991); meta-analytic extension through environmental epidemiology consortia (1990s–2000s)	Nathan Mantel (1973); D. C. Thomas (1977 formalization)
ประเภท	Observational epidemiological design with meta-analytic synthesis	Hybrid observational study design
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	3	6
สรุป	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.