Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchambuzi wa Kimaendeleo wa Uhusiano kati ya Kipimo na Mwitikio× | Mfumo wa Cox Proportional Hazards× | |
|---|---|---|
| Nyanja | Epidemiolojia | Epidemiolojia |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1965 (Hill's criteria); widely applied through 1980s–present | 1972 |
| Mwanzilishi≠ | Bradford Hill (causal criteria including dose-response, 1965); formalized in modern epidemiology by Rothman, Greenland and others | Sir David Roxbee Cox |
| Aina≠ | Analytical epidemiological study design | Semi-parametric regression model |
| Chanzo asilia≠ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 | 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 | prospective exposure-response analysis, prospective trend analysis, forward-looking dose-response study, prospective gradient analysis | Cox regression, Cox PH model, proportional hazards model, CPH |
| Zinazohusiana≠ | 4 | 5 |
| Muhtasari≠ | Prospective dose-response analysis is an epidemiological approach that measures exposure levels in a defined population before outcomes occur, then quantifies how the risk or magnitude of an outcome changes systematically as exposure increases. By collecting exposure data prospectively, researchers can establish temporal sequence, reduce recall bias, and assess whether a biological gradient — one of Hill's classic causal criteria — exists between the agent of interest and a health outcome. | The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research. |
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