مقایسهٔ روشها
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| مطالعه موردی-شاهدی تودرتوی چندمرکزی× | مطالعه مورد-شاهدی چندمرکزی× | |
|---|---|---|
| حوزه | اپیدمیولوژی | اپیدمیولوژی |
| خانواده | Process / pipeline | Process / pipeline |
| سال پیدایش≠ | 1990s–2000s (multicenter adaptation) | Mid-20th century; multicenter framework formalised 1970s–1980s |
| پدیدآور≠ | Nested case-control: Norman Mantel (1973); multicenter extension widely adopted in EPIC and other large consortium studies (1990s–2000s) | Epidemiology convention; seminal statistical framework by Breslow & Day (IARC, 1980) |
| نوع≠ | Observational analytical study design | Observational analytical epidemiological design |
| منبع بنیادین≠ | 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. link ↗ | 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 |
| نامهای دیگر | multicenter NCC, multi-site nested case-control, pooled nested case-control, nested case-control within multicenter cohort | multisite case-control study, collaborative case-control study, pooled case-control study, multi-institutional case-control study |
| مرتبط | 6 | 6 |
| خلاصه≠ | A multicenter nested case-control study embeds a case-control analysis within two or more geographically or institutionally distinct prospective cohorts. Cases who develop the outcome of interest are identified across all participating sites, then matched to controls sampled from the same risk sets, enabling pooled estimation of exposure-disease associations with greater statistical power and geographic generalizability than any single-center nested design. | 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. |
| ScholarGateمجموعهداده ↗ |
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