Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Estudio de Cohorte Adaptativo× | Análisis de Series Temporales Interrumpidas (ITS)× | |
|---|---|---|
| Campo≠ | Epidemiología | Inferencia causal |
| Familia≠ | Process / pipeline | Regression model |
| Año de origen≠ | 2000s–2010s (systematic formalisation) | 2002 |
| Autor original≠ | 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) |
| Tipo≠ | Observational / adaptive epidemiological design | Quasi-experimental segmented regression |
| Fuente seminal≠ | 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 ↗ |
| Alias≠ | adaptive longitudinal study, flexible cohort design, adaptive prospective cohort, ACS | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Relacionados≠ | 4 | 5 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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