השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מחקר אפידמיולוגי חתך מותאם× | מחקר אפידמיולוגי חתך× | |
|---|---|---|
| תחום | אפידמיולוגיה | אפידמיולוגיה |
| משפחה | Process / pipeline | Process / pipeline |
| שנת המקור≠ | Mid-to-late 20th century (formalized ~1970s–1990s) | 1960s (formal codification); widely practiced since mid-20th century |
| הוגה השיטה≠ | 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) |
| סוג≠ | Observational epidemiological study design | Observational, descriptive/analytic epidemiological design |
| מקור מכונן≠ | 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 |
| כינויים | 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 |
| קשורות≠ | 5 | 6 |
| תקציר≠ | 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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