قارن الطرق

	تحليل الاستجابة للجرعة المتطابقة	تحليل العلاقة بين الجرعة والاستجابة
المجال	علم الأوبئة	علم الأوبئة
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	1970s–1980s	Conceptual roots 16th century; modern epidemiological application mid-20th century
صاحب الطريقة	Developed within the matched case-control framework; formalized by Breslow and Day (1980) and Rothman and colleagues	Paracelsus (conceptual foundation); formalized by John Snow and later Bradford Hill
النوع	Analytical epidemiological method	Quantitative analytical method
المصدر التأسيسي	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
الأسماء البديلة	matched trend analysis, dose-response in matched designs, exposure-response analysis with matching, matched exposure-gradient analysis	exposure-response analysis, concentration-response modeling, dose-response modeling, DRA
ذات صلة	4	4
الملخص	Matched dose-response analysis evaluates whether increasing levels of exposure are associated with proportionally increasing (or decreasing) risk of an outcome, within a study where cases and controls — or exposed and unexposed individuals — have been deliberately matched on key confounders such as age, sex, or study site. Matching controls residual confounding structurally, while the dose-response component tests whether the exposure-outcome relationship follows a biologically plausible gradient, strengthening causal inference.	Dose-response analysis quantifies the relationship between the magnitude of an exposure (the dose) and the probability or rate of an outcome (the response). It is a core analytical strategy in epidemiology and toxicology, providing evidence that increasing exposure systematically increases — or decreases — the risk of disease. A demonstrated dose-response gradient is one of Bradford Hill's classic criteria supporting causal inference.