Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Estudi de casos i controls amb ajustament de risc× | Regressió Logística× | |
|---|---|---|
| Camp≠ | Epidemiologia | Estadística per a la recerca |
| Família | Process / pipeline | Process / pipeline |
| Any d'origen≠ | 1950s–1980s (case-control design from 1950; risk-adjustment conventions established by 1980s) | 1958 |
| Autor original≠ | Doll & Hill (foundational case-control); risk adjustment via multivariate logistic regression systematised by Schlesselman (1982) and Breslow & Day (1980) | David Roxbee Cox |
| Tipus≠ | Observational analytic study design | Method |
| Font seminal≠ | Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press. ISBN: 978-0195029697 | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Àlies≠ | adjusted case-control study, covariate-adjusted case-control, risk-stratified case-control study, matched and adjusted case-control study | logit model, binomial logistic regression, LR |
| Relacionats≠ | 5 | 3 |
| Resum≠ | A risk-adjusted case-control study is an observational design that identifies individuals with a disease outcome (cases) and comparable individuals without it (controls), then uses statistical adjustment — most commonly multivariable logistic regression — to estimate the association between an exposure and the outcome while controlling for confounding risk factors. The adjustment step is what distinguishes this variant from a simple case-control study, producing odds ratios that better reflect the independent contribution of the exposure of interest. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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