Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Дослідження Фази IV з підбором пар× | Аналіз перерваних часових рядів (ITS)× | |
|---|---|---|
| Галузь≠ | Епідеміологія | Причинно-наслідковий висновок |
| Родина≠ | Process / pipeline | Regression model |
| Рік появи≠ | 1980s–1990s (formalized in post-marketing regulatory frameworks) | 2002 |
| Автор методу≠ | Regulatory tradition (FDA, EMA); matching methodology from Rosenbaum & Rubin (1983) | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Тип≠ | Observational study design | Quasi-experimental segmented regression |
| Основоположне джерело≠ | Strom, B. L., & Kimmel, S. E. (Eds.). (2005). Textbook of Pharmacoepidemiology. Wiley. ISBN: 978-0470029244 | 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 ↗ |
| Інші назви≠ | matched post-marketing surveillance study, Phase IV matched cohort study, matched pharmacoepidemiological study, post-authorization matched safety study | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Пов'язані | 5 | 5 |
| Підсумок≠ | A Matched Phase IV study is a post-marketing observational design in which patients who received an approved drug (or intervention) are matched to comparable non-exposed patients — or patients on an alternative therapy — to evaluate real-world safety, effectiveness, or long-term outcomes. Conducted after regulatory approval, it combines the epidemiological rigour of matching with the breadth of post-authorization pharmacovigilance, generating evidence that randomized trials are rarely powered or timed to provide. | 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. |
| ScholarGateНабір даних ↗ |
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