方法对比
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| 中断时间序列（ITS）分析× | 倾向得分匹配× | |
|---|---|---|
| 领域≠ | 因果推断 | 研究统计学 |
| 方法族≠ | Regression model | Process / pipeline |
| 起源年份≠ | 2002 | 1983 |
| 提出者≠ | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) | Paul Rosenbaum and Donald Rubin |
| 类型≠ | Quasi-experimental segmented regression | Method |
| 开创性文献≠ | Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355. DOI ↗ | 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 ↗ |
| 别名 | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi | PSM, propensity score weighting, covariate balance |
| 相关≠ | 5 | 3 |
| 摘要≠ | Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope. | 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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