Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzcentru ligzdotā gadījumu-kontroles pētījuma× | Daudzkodolu kohortas pētījums× | |
|---|---|---|
| Nozare | Epidemioloģija | Epidemioloģija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1990s–2000s (multicenter adaptation) | Mid-to-late 20th century (widespread adoption 1970s–1990s) |
| Autors≠ | 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) |
| Tips≠ | Observational analytical study design | Observational longitudinal study |
| Pirmavots≠ | 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 |
| Citi nosaukumi | 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 |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | 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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