方法对比
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| 配对横断面流行病学研究× | 横断面流行病学研究× | |
|---|---|---|
| 领域 | 流行病学 | 流行病学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | Mid-to-late 20th century (formalized ~1970s–1990s) | 1960s (formal codification); widely practiced since mid-20th century |
| 提出者≠ | 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) |
| 类型≠ | Observational epidemiological study design | Observational, descriptive/analytic epidemiological design |
| 开创性文献≠ | 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 |
| 别名 | 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 |
| 相关≠ | 5 | 6 |
| 摘要≠ | 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. |
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