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| Adaptive Cohort Study× | 시계열 단절 분석 (Interrupted Time Series, ITS)× | |
|---|---|---|
| 분야≠ | 역학 | 인과추론 |
| 계열≠ | Process / pipeline | Regression model |
| 기원 연도≠ | 2000s–2010s (systematic formalisation) | 2002 |
| 창시자≠ | Extension of classic cohort methods; adaptive design principles formalised by regulatory and epidemiology communities in the 2000s–2010s | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| 유형≠ | Observational / adaptive epidemiological design | Quasi-experimental segmented regression |
| 원전≠ | VanderWeele, T. J., & Hernan, M. A. (2012). Results on differential and dependent measurement error of the exposure and the outcome using signed directed acyclic graphs. American Journal of Epidemiology, 175(12), 1303–1310. DOI ↗ | 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 ↗ |
| 별칭≠ | adaptive longitudinal study, flexible cohort design, adaptive prospective cohort, ACS | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| 관련≠ | 4 | 5 |
| 요약≠ | An adaptive cohort study is a longitudinal observational design that follows a defined group of individuals over time to assess exposure-outcome relationships, while incorporating pre-specified adaptation rules that allow protocol modifications — such as sample-size re-estimation, subgroup enrichment, or measurement schedule adjustments — based on accumulating interim data. Adaptations are made without compromising validity, guided by a statistical analysis plan agreed upon before data collection begins. | 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. |
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