Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Usajili wa Hatari za Uwiano wa Cox wa Vituo Vingi× | Utafiti wa Kikundi Kazi wa Vituo Vingi× | |
|---|---|---|
| Nyanja | Epidemiolojia | Epidemiolojia |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1972 (Cox model); multicenter applications formalized 1980s–1990s | Mid-to-late 20th century (widespread adoption 1970s–1990s) |
| Mwanzilishi≠ | D. R. Cox (Cox PH model); multicenter extension developed through collaborative trial methodology | Developed incrementally through large collaborative epidemiological projects (e.g., Framingham Heart Study consortium expansions, 1948 onward; EPIC study, 1992) |
| Aina≠ | Semi-parametric survival regression for clustered data | Observational longitudinal study |
| Chanzo asilia≠ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. DOI ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| Majina mbadala | multicenter Cox regression, multisite Cox PH model, stratified Cox model across centers, multicenter survival regression | multisite cohort study, multi-centre cohort, collaborative cohort study, pooled cohort study |
| Zinazohusiana≠ | 4 | 6 |
| Muhtasari≠ | Multicenter Cox proportional hazards regression extends the classic Cox PH model to studies conducted at two or more clinical sites or centers. It estimates the effect of predictors on time-to-event outcomes while explicitly accounting for clustering within centers, between-center heterogeneity, and potential differences in baseline hazard across sites. This design is standard practice in large multicenter RCTs and observational cohort studies in oncology, cardiology, and other clinical fields. | 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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