So sánh phương pháp

	Thiết kế thử nghiệm chéo thích ứng	Thiết kế Thí nghiệm Thừa số Chéo
Lĩnh vực	Thiết kế thí nghiệm	Thiết kế thí nghiệm
Họ	Process / pipeline	Process / pipeline
Năm ra đời	Late 1990s–2000s	1920s–1960s (synthesis of factorial and crossover traditions)
Người khởi xướng	Developed through convergence of crossover trial methodology (Senn, Williams) and adaptive design methods (Bauer, Köhne, Chow, Chang)	R. A. Fisher (factorial principles, 1920s); crossover integration developed in biostatistics through mid-20th century
Loại	Experimental design — hybrid adaptive/crossover	Experimental design
Công trình gốc	Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman & Hall/CRC. ISBN: 978-1584888468	Jones, B., & Kenward, M. G. (2014). Design and Analysis of Cross-Over Trials (3rd ed.). Chapman and Hall/CRC. ISBN: 978-1439861424
Tên gọi khác	adaptive crossover trial, adaptive crossover design, crossover adaptive trial, ACE design	within-subject factorial design, repeated-measures factorial experiment, factorial crossover trial, crossover factorial trial
Liên quan	5	5
Tóm tắt	An adaptive crossover experiment combines the within-subject efficiency of crossover designs — where each participant receives multiple treatments in sequence — with pre-specified adaptive rules that allow trial parameters to be modified based on interim data. Each participant acts as their own control across treatment periods, while ongoing accumulating evidence can trigger pre-planned changes such as sample size re-estimation, treatment arm dropping, or allocation ratio adjustment, all governed by a formal adaptation plan to preserve inferential validity.	A crossover factorial experiment combines two powerful design principles: factorial structure, which studies multiple factors and their interactions simultaneously, and crossover structure, in which each participant receives more than one treatment combination across sequential periods. By serving as their own control, participants reduce between-subject variability, improving statistical power while also revealing how different factor levels interact within the same individual.