方法对比
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| 风险调整病例交叉设计× | 风险调整队列研究× | |
|---|---|---|
| 领域 | 流行病学 | 流行病学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1991 (base design); risk-adjustment extensions from mid-1990s onward | Mid–late 20th century (risk-adjusted cohort designs systematized by 1970s–1990s) |
| 提出者≠ | Malcolm Maclure (case-crossover base); extensions incorporating covariate risk adjustment developed in subsequent pharmacoepidemiology literature | Evolution of cohort study methodology; risk adjustment formalized through work of Rothman, Greenland, and others in epidemiology, 20th century |
| 类型≠ | Observational analytic epidemiological design | Observational epidemiological study design with statistical confounding control |
| 开创性文献≠ | Maclure, M. (1991). The case-crossover design: a method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153. DOI ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| 别名 | adjusted case-crossover study, covariate-adjusted case-crossover, risk-controlled case-crossover, case-crossover with risk adjustment | adjusted cohort study, covariate-adjusted cohort, risk-controlled prospective study, propensity-adjusted cohort |
| 相关 | 4 | 4 |
| 摘要≠ | The risk-adjusted case-crossover design is a self-matched epidemiological method that compares a person's exposure during a brief hazard window immediately preceding an acute event to their exposure during one or more control windows from the same individual, while formally accounting for time-varying or time-fixed covariates that could confound the exposure-event relationship. By using each case as their own control, stable individual-level confounders are automatically cancelled, while covariate adjustment handles residual time-varying risks. | A risk-adjusted cohort study is an observational epidemiological design in which a defined group of individuals is followed over time to compare outcomes between exposed and unexposed subgroups, with statistical methods applied to control for measured confounders. Adjustment strategies — including multivariable regression, propensity score matching, inverse probability weighting, or standardization — are used to reduce bias and produce effect estimates that more closely approximate what would be observed in a randomized trial. |
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