قارن الطرق

	تجربة سريرية عشوائية للمرحلة الثالثة معدلة حسب المخاطر	دراسة الأتراب المعدلة بالمخاطر
المجال	علم الأوبئة	علم الأوبئة
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	1980s–present	Mid–late 20th century (risk-adjusted cohort designs systematized by 1970s–1990s)
صاحب الطريقة	Evolving practice; foundational risk-adjustment principles established by Pocock (1983) and extended by numerous trialists	Evolution of cohort study methodology; risk adjustment formalized through work of Rothman, Greenland, and others in epidemiology, 20th century
النوع	Confirmatory randomized trial with baseline risk stratification and covariate adjustment	Observational epidemiological study design with statistical confounding control
المصدر التأسيسي	Pocock, S. J. (1983). Clinical Trials: A Practical Approach. Wiley. ISBN: 978-0471901556	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641
الأسماء البديلة	risk-stratified Phase III trial, covariate-adjusted Phase III RCT, risk-adjusted confirmatory trial, RA-Phase III	adjusted cohort study, covariate-adjusted cohort, risk-controlled prospective study, propensity-adjusted cohort
ذات صلة	6	4
الملخص	A risk-adjusted Phase III clinical trial is a large-scale confirmatory randomized experiment that explicitly incorporates participants' baseline prognostic risk profile into both the randomization process and the primary statistical analysis. By stratifying patients on known risk factors before allocation and adjusting for those factors in the outcome model, the design achieves greater statistical precision, reduces confounding, and produces treatment effect estimates that are more clinically meaningful across patient subgroups.	A risk-adjusted cohort study is an observational epidemiological design in which a defined group of individuals is followed over time to compare outcomes between exposed and unexposed subgroups, with statistical methods applied to control for measured confounders. Adjustment strategies — including multivariable regression, propensity score matching, inverse probability weighting, or standardization — are used to reduce bias and produce effect estimates that more closely approximate what would be observed in a randomized trial.