Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Utafiti wa Kikundi cha Adaptive× | Utafiti wa Kikundi cha Wanafunzi wanaotarajiwa× | |
|---|---|---|
| Nyanja | Epidemiolojia | Epidemiolojia |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2000s–2010s (systematic formalisation) | 1950s (systematic application); conceptual roots earlier |
| Mwanzilishi≠ | Extension of classic cohort methods; adaptive design principles formalised by regulatory and epidemiology communities in the 2000s–2010s | Richard Doll and Austin Bradford Hill (landmark application, 1951-1954); cohort methodology formalised by modern epidemiology textbooks |
| Aina≠ | Observational / adaptive epidemiological design | Observational longitudinal study design |
| Chanzo asilia≠ | 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 ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| Majina mbadala | adaptive longitudinal study, flexible cohort design, adaptive prospective cohort, ACS | longitudinal cohort study, prospective follow-up study, incidence study, prospective observational cohort |
| Zinazohusiana≠ | 4 | 6 |
| Muhtasari≠ | 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. | A prospective cohort study assembles a group of participants who are free of the outcome of interest at baseline, measures their exposures, and then follows them forward in time to record who develops the outcome. By collecting exposure data before outcomes occur, it establishes a clear temporal sequence that supports causal inference — a major advantage over retrospective designs. It is the cornerstone observational method in epidemiology and clinical research. |
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