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| Prospektiv Cox proportionella riskmodellen× | Prospektiv kohortstudie× | |
|---|---|---|
| Ämnesområde | Epidemiologi | Epidemiologi |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 1972 (Cox model); widespread prospective application from late 1970s | 1950s (systematic application); conceptual roots earlier |
| Upphovsperson≠ | David R. Cox (model); applied prospectively in large cohort studies from 1970s onward | Richard Doll and Austin Bradford Hill (landmark application, 1951-1954); cohort methodology formalised by modern epidemiology textbooks |
| Typ≠ | Semi-parametric survival regression applied to prospectively collected time-to-event data | Observational longitudinal study design |
| Ursprungskälla≠ | 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 |
| Alias | prospective Cox regression, Cox PH prospective study, prospective survival regression, prospective hazard modeling | longitudinal cohort study, prospective follow-up study, incidence study, prospective observational cohort |
| Närliggande≠ | 4 | 6 |
| Sammanfattning≠ | Prospective Cox proportional hazards regression combines a forward-looking cohort design — in which participants are enrolled before outcomes occur and followed over time — with Cox's semi-parametric survival model. The method estimates how baseline covariates measured at enrollment influence the rate at which participants experience a time-to-event outcome, while preserving the temporal direction required for causal inference. It is one of the most widely used analytical frameworks in clinical epidemiology and chronic disease research. | 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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