Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchambuzi wa Hatari Zinazoshindana Katika Vituo Vingi× | Usajili wa Hatari za Uwiano wa Cox wa Vituo Vingi× | |
|---|---|---|
| Nyanja | Epidemiolojia | Epidemiolojia |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1999 (Fine-Gray); extended to multicenter settings throughout 2000s–2010s | 1972 (Cox model); multicenter applications formalized 1980s–1990s |
| Mwanzilishi≠ | Fine & Gray (subdistribution hazard model); Prentice et al. (cause-specific hazard model) | D. R. Cox (Cox PH model); multicenter extension developed through collaborative trial methodology |
| Aina≠ | Survival / time-to-event statistical analysis | Semi-parametric survival regression for clustered data |
| Chanzo asilia≠ | Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509. DOI ↗ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. DOI ↗ |
| Majina mbadala | multicenter CRA, multi-site competing risks, multicenter cumulative incidence analysis, polycentric competing risks study | multicenter Cox regression, multisite Cox PH model, stratified Cox model across centers, multicenter survival regression |
| Zinazohusiana | 4 | 4 |
| Muhtasari≠ | Multicenter competing risks analysis is a time-to-event method applied across multiple clinical centers to estimate the probability of a specific event of interest when other mutually exclusive events — competing risks — can preclude its occurrence. By pooling data from diverse sites, it achieves the sample sizes needed to model rare events and enables assessment of center-level variation in cumulative incidence and covariate effects. | 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. |
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