เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| Multicenter Nested Case-Control× | การศึกษาแบบกลุ่มผู้ร่วมวิจัยหลายศูนย์ (Multicenter Cohort Study)× | |
|---|---|---|
| สาขาวิชา | ระบาดวิทยา | ระบาดวิทยา |
| ตระกูล | Process / pipeline | Process / pipeline |
| ปีกำเนิด≠ | 1990s–2000s (multicenter adaptation) | Mid-to-late 20th century (widespread adoption 1970s–1990s) |
| ผู้ริเริ่ม≠ | Nested case-control: Norman Mantel (1973); multicenter extension widely adopted in EPIC and other large consortium studies (1990s–2000s) | Developed incrementally through large collaborative epidemiological projects (e.g., Framingham Heart Study consortium expansions, 1948 onward; EPIC study, 1992) |
| ประเภท≠ | Observational analytical study design | Observational longitudinal study |
| แหล่งต้นตำรับ≠ | 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 ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| ชื่อเรียกอื่น | multicenter NCC, multi-site nested case-control, pooled nested case-control, nested case-control within multicenter cohort | multisite cohort study, multi-centre cohort, collaborative cohort study, pooled cohort study |
| ที่เกี่ยวข้อง | 6 | 6 |
| สรุป≠ | 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 cohort study follows defined groups of participants at two or more geographically or institutionally distinct sites over time to estimate incidence, identify risk factors, and quantify associations between exposures and outcomes. By pooling data from multiple centers, it achieves statistical power and population diversity that single-site designs cannot match, making it the workhorse of large-scale epidemiological and clinical research. |
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