Võrdle meetodeid
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| Mitmekeskusteline juht-kontroll-uuring× | Meta-analüütiline juhtum-kontrolluuring× | |
|---|---|---|
| Valdkond | Epidemioloogia | Epidemioloogia |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | Mid-20th century; multicenter framework formalised 1970s–1980s | 1980s–2000 (formalized with MOOSE reporting guidelines in 2000) |
| Looja≠ | Epidemiology convention; seminal statistical framework by Breslow & Day (IARC, 1980) | Systematic development attributed to multiple epidemiologists; MOOSE guidelines formalized by Stroup et al. |
| Tüüp≠ | Observational analytical epidemiological design | Observational study synthesis |
| Algallikas≠ | Breslow, N. E., & Day, N. E. (1980). Statistical Methods in Cancer Research. Volume I: The Analysis of Case-Control Studies. IARC Scientific Publications No. 32. International Agency for Research on Cancer, Lyon. ISBN: 978-9283211327 | Shapiro, S. (1994). Meta-analysis/Shmeta-analysis. American Journal of Epidemiology, 140(9), 771-778. DOI ↗ |
| Rööpnimetused | multisite case-control study, collaborative case-control study, pooled case-control study, multi-institutional case-control study | pooled case-control analysis, case-control meta-analysis, meta-analytic case-control design, systematic pooled case-control |
| Seotud≠ | 6 | 4 |
| Kokkuvõte≠ | A multicenter case-control study is an observational design that identifies individuals who have developed a disease (cases) and disease-free comparators (controls) across two or more study sites simultaneously. By pooling recruitment across hospitals, clinics, or geographic regions, the design achieves larger sample sizes, captures exposure variability over broader populations, and improves the statistical power needed to detect modest odds ratios for rare or heterogeneous diseases. | A meta-analytic case-control study systematically identifies, critically appraises, and quantitatively synthesizes data from multiple independent case-control studies examining the same exposure-disease relationship. By pooling odds ratios across studies, it yields a more precise and generalizable estimate of association than any single study can provide, while formally quantifying heterogeneity across populations, settings, and study periods. |
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