Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Эпидемиологическое поперечное исследование с сопоставлением× | Поперечное эпидемиологическое исследование× | |
|---|---|---|
| Область | Эпидемиология | Эпидемиология |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | Mid-to-late 20th century (formalized ~1970s–1990s) | 1960s (formal codification); widely practiced since mid-20th century |
| Автор метода≠ | Developed within the tradition of observational epidemiology; matching principles codified by Greenland, Rothman, and Kelsey in modern epidemiology texts | Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s) |
| Тип≠ | Observational epidemiological study design | Observational, descriptive/analytic epidemiological design |
| Основополагающий источник≠ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 | Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press. ISBN: 978-0195080407 |
| Другие названия | matched cross-sectional survey, matched prevalence study, matched cross-sectional design, frequency-matched cross-sectional study | prevalence study, cross-sectional survey, transversal study, cross-sectional design |
| Связанные≠ | 5 | 6 |
| Сводка≠ | A matched cross-sectional epidemiological study is an observational design that measures exposure and outcome simultaneously in a population sample while applying matching to control for one or more confounding variables. By pairing or grouping participants on key characteristics such as age, sex, or socioeconomic status before or during analysis, the design reduces confounding bias without requiring longitudinal follow-up, making it efficient for estimating prevalence and cross-sectional associations. | A cross-sectional epidemiological study measures the exposure(s) and outcome(s) of interest simultaneously in a defined population at a single point in time (or over a short period). Because there is no follow-up, it is the most efficient observational design for estimating disease prevalence and for generating hypotheses about associations between risk factors and health outcomes. |
| ScholarGateНабор данных ↗ |
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