مقایسهٔ روش‌ها

	کارآزمایی بالینی فاز III تطبیقی	تطابق امتیاز تمایل (Propensity Score Matching)
حوزه	اپیدمیولوژی	آمار پژوهش
خانواده	Process / pipeline	Process / pipeline
سال پیدایش	Mid-20th century (matching in RCTs formalized ~1950s–1970s)	1983
پدیدآور	Fisher, R. A. (matching principles); adapted into confirmatory trial design over mid-20th century	Paul Rosenbaum and Donald Rubin
نوع	Controlled confirmatory clinical trial with matching	Method
منبع بنیادین	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
نام‌های دیگر	matched controlled Phase III trial, Phase III matched-pair trial, matched confirmatory trial, matched late-phase RCT	PSM, propensity score weighting, covariate balance
مرتبط	5	3
خلاصه	A matched Phase III clinical trial is a confirmatory, late-stage controlled study in which each participant assigned to the experimental treatment is paired with one or more controls who share key prognostic characteristics — such as age, disease stage, or comorbidities — before treatment allocation. By ensuring baseline comparability at the level of matched pairs, the design reduces confounding and improves statistical efficiency in settings where simple randomization alone may produce imbalanced groups or where full randomization is logistically or ethically constrained.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.