Сравнение на методи

	Рисково-коригирано клинично изпитване Фаза III	Мачово рандомизирано клинично изпитване
Област	Епидемиология	Епидемиология
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1980s–present	Mid-20th century concept; methodological formalization circa 2000–2010
Създател	Evolving practice; foundational risk-adjustment principles established by Pocock (1983) and extended by numerous trialists	Developed formally in biostatistics literature; Greevy, Imai and colleagues advanced modern frameworks in the 2000s
Тип	Confirmatory randomized trial with baseline risk stratification and covariate adjustment	Experimental clinical study design
Основополагащ източник	Pocock, S. J. (1983). Clinical Trials: A Practical Approach. Wiley. ISBN: 978-0471901556	Imai, K., King, G., & Nall, C. (2009). The essential role of pair matching in cluster-randomized experiments, with application to the Mexican universal health insurance evaluation. Statistical Science, 24(1), 29–53.
Други названия	risk-stratified Phase III trial, covariate-adjusted Phase III RCT, risk-adjusted confirmatory trial, RA-Phase III	matched RCT, matched-pair randomized trial, matched randomized controlled trial, covariate-matched RCT
Свързани	6	6
Резюме	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 matched randomized clinical trial pairs participants (or clusters) on key baseline characteristics before randomization, then allocates one member of each pair to treatment and the other to control. This design combines the causal validity of randomization with the covariate balance of matching, increasing statistical efficiency and reducing confounding from known prognostic variables without sacrificing the internal validity of a controlled experiment.