Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzcentru devas-atbildes analīze× | Daudzkodolu kohortas pētījums× | |
|---|---|---|
| Nozare | Epidemioloģija | Epidemioloģija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1992 (foundational trend methods); refined 2000s–2010s | Mid-to-late 20th century (widespread adoption 1970s–1990s) |
| Autors≠ | Greenland & Longnecker; extended by Orsini et al. | Developed incrementally through large collaborative epidemiological projects (e.g., Framingham Heart Study consortium expansions, 1948 onward; EPIC study, 1992) |
| Tips≠ | Quantitative epidemiological analysis | Observational longitudinal study |
| Pirmavots≠ | Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American Journal of Epidemiology, 135(11), 1301-1309. DOI ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| Citi nosaukumi | pooled dose-response analysis, multicenter exposure-response analysis, multi-site dose-response modeling, collaborative dose-response study | multisite cohort study, multi-centre cohort, collaborative cohort study, pooled cohort study |
| Saistītās≠ | 2 | 6 |
| Kopsavilkums≠ | Multicenter dose-response analysis estimates the quantitative shape of the relationship between a graded exposure and a health outcome by pooling data or effect estimates across two or more study centers. Using flexible regression tools such as restricted cubic splines or fractional polynomials within a two-stage meta-analytic framework, it characterizes whether the relationship is linear, supra-linear, threshold-based, or J-shaped — providing far greater statistical power and generalizability than any single center could achieve alone. | 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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