เปรียบเทียบวิธี

	การศึกษาทางระบาดวิทยาภาคตัดขวางแบบจับคู่	การศึกษาแบบกลุ่มที่จับคู่กัน
สาขาวิชา	ระบาดวิทยา	ระบาดวิทยา
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	Mid-to-late 20th century (formalized ~1970s–1990s)	Mid-20th century; propensity-score variant 1983
ผู้ริเริ่ม	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)
ประเภท	Observational epidemiological study design	Observational analytic study design
แหล่งต้นตำรับ	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
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	5	5
สรุป	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.