Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Адаптивне епідеміологічне дослідження перехресного типу× | Екологічне дослідження× | |
|---|---|---|
| Галузь | Епідеміологія | Епідеміологія |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1990s–2000s (formalization of adaptive elements in observational surveys) | 19th century (Snow 1854); formalised mid-20th century |
| Автор методу≠ | Conceptual synthesis of adaptive design methods (Wald, 1947; Bauer & Kohne, 1994) with classical cross-sectional epidemiology (MacMahon & Pugh, 1960s) | Various; foundational work by John Snow (1854) and systematised in modern form by Brian MacMahon and colleagues |
| Тип≠ | Observational epidemiological study design | Observational epidemiological study |
| Основоположне джерело≠ | Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press. ISBN: 978-0195083439 | Morgenstern, H. (1995). Ecologic studies in epidemiology: concepts, principles, and methods. Annual Review of Public Health, 16(1), 61–81. DOI ↗ |
| Інші назви | adaptive cross-sectional survey, adaptive prevalence study, adaptive epidemiological survey design, adaptive population cross-section | aggregate study, correlational study, ecological correlation study, population-level study |
| Пов'язані≠ | 2 | 5 |
| Підсумок≠ | An adaptive cross-sectional epidemiological study combines the core logic of a cross-sectional survey — measuring exposures and outcomes simultaneously in a defined population at one point in time — with pre-specified adaptive rules that allow modifications to sampling strategy, sample size, or subgroup allocation based on accumulating interim data. The approach preserves the efficiency and speed of a standard cross-sectional design while improving precision for rare exposures or heterogeneous populations by redirecting sampling resources in real time. | An ecological study is an observational epidemiological design in which the unit of analysis is a group or population — a country, region, city, or time period — rather than an individual. Exposures and outcomes are measured as aggregates (rates, proportions, or means) and then correlated across groups to generate or evaluate hypotheses about population-level associations between risk factors and disease. |
| ScholarGateНабір даних ↗ |
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