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| Analiza prospektywna ryzyka konkurencyjnego× | Badanie kohortowe prospektywne× | |
|---|---|---|
| Dziedzina | Epidemiologia | Epidemiologia |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1978–1999 (foundational frameworks; prospective application standard by 2000s) | 1950s (systematic application); conceptual roots earlier |
| Twórca≠ | Fine & Gray (subdistribution hazard model, 1999); Prentice, Kalbfleisch et al. (cause-specific hazard, 1978) | Richard Doll and Austin Bradford Hill (landmark application, 1951-1954); cohort methodology formalised by modern epidemiology textbooks |
| Typ≠ | Observational analytic study with event-time statistical analysis | Observational longitudinal study design |
| Źródło pierwotne≠ | 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 ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| Inne nazwy | prospective CRA, prospective subdistribution hazard analysis, prospective cause-specific hazard analysis, forward-looking competing events analysis | longitudinal cohort study, prospective follow-up study, incidence study, prospective observational cohort |
| Pokrewne≠ | 4 | 6 |
| Podsumowanie≠ | Prospective competing risks analysis is an observational study design that follows participants forward in time from a well-defined starting point, recording all events — including those that prevent the primary event from occurring — and then estimates cause-specific incidence while correctly accounting for competing outcomes. It combines the temporal clarity of prospective cohort follow-up with the statistical rigor of competing risks methodology to avoid the overestimation inherent in standard Kaplan-Meier curves when multiple event types are present. | A prospective cohort study assembles a group of participants who are free of the outcome of interest at baseline, measures their exposures, and then follows them forward in time to record who develops the outcome. By collecting exposure data before outcomes occur, it establishes a clear temporal sequence that supports causal inference — a major advantage over retrospective designs. It is the cornerstone observational method in epidemiology and clinical research. |
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