方法对比
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| 多中心巢式病例对照研究× | 多中心病例对照研究× | |
|---|---|---|
| 领域 | 流行病学 | 流行病学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s–2000s (multicenter adaptation) | Mid-20th century; multicenter framework formalised 1970s–1980s |
| 提出者≠ | 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) |
| 类型≠ | Observational analytical study design | Observational analytical epidemiological design |
| 开创性文献≠ | 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 |
| 别名 | 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 |
| 相关 | 6 | 6 |
| 摘要≠ | 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. |
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