Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Адаптивный дизайн исследования× | Анализ прерванных временных рядов (Interrupted Time Series, ITS)× | |
|---|---|---|
| Область≠ | Клинические исследования | Причинно-следственный вывод |
| Семейство≠ | Process / pipeline | Regression model |
| Год появления≠ | 1990s-2000s | 2002 |
| Автор метода≠ | Stephen Pocock, Christopher Jennison, and statistical methodologists; FDA formalized guidance 2019 | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Тип≠ | Research Design | Quasi-experimental segmented regression |
| Основополагающий источник≠ | Pocock, S. J. (2005). Current issues in the design and interpretation of clinical trials. BMJ, 330(7500), 1118–1121. 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 ↗ |
| Другие названия≠ | adaptive trial, adaptive design, response-adaptive randomization, RAR | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Связанные≠ | 1 | 5 |
| Сводка≠ | An adaptive trial design allows pre-specified modifications to the trial based on interim data—such as sample size re-estimation, stopping for futility or efficacy, dropping ineffective arms, or shifting randomization ratios toward better-performing treatments. Developed systematically in the 1990s–2000s by statisticians like Pocock and Jennison, and formalized by the FDA in 2019, adaptive designs accelerate drug development, reduce exposure to ineffective treatments, and improve efficiency without inflating false-positive rates when properly executed. | 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. |
| ScholarGateНабор данных ↗ |
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