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| Adaptive Ecological Study× | Analyse von unterbrochenen Zeitreihen (Interrupted Time Series, ITS)× | |
|---|---|---|
| Fachgebiet≠ | Epidemiologie | Kausale Inferenz |
| Familie≠ | Process / pipeline | Regression model |
| Entstehungsjahr≠ | 1990s–2000s (adaptive extensions of classical ecological designs) | 2002 |
| Urheber≠ | Building on classical ecological epidemiology (Durkheim, Snow, Morgenstern); adaptive extensions developed in late 20th–early 21st century methodological literature | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Typ≠ | Observational study design | Quasi-experimental segmented regression |
| Wegweisende Quelle≠ | Morgenstern, H. (1998). Ecologic studies. In K. J. Rothman & S. Greenland (Eds.), Modern Epidemiology (2nd ed., pp. 459–480). Lippincott-Raven. link ↗ | 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 ↗ |
| Aliasnamen≠ | adaptive ecologic study, sequential ecological study, adaptive population-level design, adaptive group-level study | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Verwandt≠ | 3 | 5 |
| Zusammenfassung≠ | An adaptive ecological study is an observational epidemiological design in which the unit of analysis is a group or population (e.g., a region, country, or community) rather than an individual. It extends the classical ecological study by incorporating pre-specified interim decision rules that allow modifications — such as changes in geographic unit, time window, or exposure categorisation — as data accumulate, while preserving overall inferential validity. The design is used to explore population-level associations between aggregate exposures and aggregate outcomes. | 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. |
| ScholarGateDatensatz ↗ |
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