เปรียบเทียบวิธี

	การศึกษาทางระบาดวิทยาภาคตัดขวางแบบจับคู่	การศึกษาทางระบาดวิทยาภาคตัดขวาง
สาขาวิชา	ระบาดวิทยา	ระบาดวิทยา
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	Mid-to-late 20th century (formalized ~1970s–1990s)	1960s (formal codification); widely practiced since mid-20th century
ผู้ริเริ่ม	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)
ประเภท	Observational epidemiological study design	Observational, descriptive/analytic epidemiological design
แหล่งต้นตำรับ	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
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	5	6
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 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.