Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Utafiti wa Kesi-na-Udhibiti Ulioboreshwa kwa Hatari× | Ulinganishaji wa Alama ya Mwelekeo× | |
|---|---|---|
| Nyanja≠ | Epidemiolojia | Takwimu za Utafiti |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1950s–1980s (case-control design from 1950; risk-adjustment conventions established by 1980s) | 1983 |
| Mwanzilishi≠ | Doll & Hill (foundational case-control); risk adjustment via multivariate logistic regression systematised by Schlesselman (1982) and Breslow & Day (1980) | Paul Rosenbaum and Donald Rubin |
| Aina≠ | Observational analytic study design | Method |
| Chanzo asilia≠ | Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press. ISBN: 978-0195029697 | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. DOI ↗ |
| Majina mbadala≠ | adjusted case-control study, covariate-adjusted case-control, risk-stratified case-control study, matched and adjusted case-control study | PSM, propensity score weighting, covariate balance |
| Zinazohusiana≠ | 5 | 3 |
| Muhtasari≠ | 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. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
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