Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Matched Cross-Sectional Epidemiological Study× | Studiu de cohortă cu perechi (Matched Cohort Study)× | |
|---|---|---|
| Domeniu | Epidemiologie | Epidemiologie |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | Mid-to-late 20th century (formalized ~1970s–1990s) | Mid-20th century; propensity-score variant 1983 |
| Autorul original≠ | Developed within the tradition of observational epidemiology; matching principles codified by Greenland, Rothman, and Kelsey in modern epidemiology texts | Established practice; propensity-score matching formalized by Rosenbaum & Rubin (1983) |
| Tip≠ | Observational epidemiological study design | Observational analytic study design |
| Sursa seminală | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| Denumiri alternative | matched cross-sectional survey, matched prevalence study, matched cross-sectional design, frequency-matched cross-sectional study | matched follow-up study, paired cohort study, propensity-matched cohort, matched prospective study |
| Înrudite | 5 | 5 |
| Rezumat≠ | 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 matched cohort study is an observational design in which each exposed participant is paired with one or more unexposed counterparts who share key characteristics — such as age, sex, or comorbidity status — before both groups are followed forward in time to compare incident outcomes. Matching controls for measured confounders at the design stage, reducing bias that would otherwise require statistical adjustment alone. |
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