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| 风险调整剂量-反应分析× | 逻辑回归× | |
|---|---|---|
| 领域≠ | 流行病学 | 研究统计学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1980s-1990s (formalized in modern epidemiology) | 1958 |
| 提出者≠ | Sander Greenland; Kenneth Rothman (foundational epidemiological methods) | David Roxbee Cox |
| 类型≠ | Epidemiological modeling technique | Method |
| 开创性文献≠ | Greenland, S. (1995). Dose-response and trend analysis in epidemiology: alternatives to categorical analysis. Epidemiology, 6(4), 356-365. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 别名≠ | confounder-adjusted dose-response, covariate-adjusted dose-response modeling, risk-stratified dose-response analysis, adjusted exposure-response analysis | logit model, binomial logistic regression, LR |
| 相关≠ | 4 | 3 |
| 摘要≠ | Risk-adjusted dose-response analysis quantifies the relationship between increasing levels of an exposure (dose) and the probability or magnitude of an outcome (response), while simultaneously controlling for baseline risk factors that could confound or modify this relationship. The method is widely applied in clinical epidemiology, pharmacoepidemiology, and environmental health research to isolate the causal contribution of exposure intensity from background risk heterogeneity among participants. | 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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