Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis meta-analítico de riesgos competitivos× | Estudio de cohortes meta-analítico× | |
|---|---|---|
| Campo | Epidemiología | Epidemiología |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 2000s–2010s (formalized as a pooled approach) | 1980s–1990s (formalized practice) |
| Autor original≠ | Based on Fine & Gray (1999) competing risks framework; meta-analytic synthesis methods established through methodological literature (mid-2000s onward) | Developed iteratively through epidemiological meta-analysis literature; Greenland, Berlin, Colditz among key contributors |
| Tipo≠ | Systematic review / meta-analysis | Quantitative synthesis / observational epidemiology |
| Fuente seminal≠ | Riley, R. D., Hayden, J. A., Steyerberg, E. W., et al. (2013). Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research. PLOS Medicine, 10(2), e1001380. DOI ↗ | 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 ↗ |
| Alias≠ | meta-analysis of competing risks, pooled competing risks analysis, systematic review competing risks | cohort meta-analysis, pooled cohort analysis, meta-analysis of cohort studies, prospective cohort meta-analysis |
| Relacionados≠ | 5 | 2 |
| Resumen≠ | Meta-analytic competing risks analysis pools results from multiple primary studies that each used a competing risks framework, allowing summary estimates of cause-specific or subdistribution hazard ratios and cumulative incidence functions. Because standard meta-analytic methods may misrepresent competing events, specialized pooling strategies are required that respect the subdistribution hazard structure introduced by Fine and Gray and the distinction between cause-specific and all-cause hazard models. | A meta-analytic cohort study systematically identifies, appraises, and statistically pools the findings of two or more independent cohort studies addressing the same exposure-outcome relationship. By combining large prospective datasets, it provides more precise risk estimates than any single cohort alone, makes dose-response patterns detectable, and enables subgroup analyses across diverse populations. It is the design of choice when cohort-level evidence exists but individual studies are underpowered or inconsistent. |
| ScholarGateConjunto de datos ↗ |
|
|