เปรียบเทียบวิธี

	การทดลองแบบไขว้แบบปรับตัว	การทดลองแบบปรับตัว
สาขาวิชา	การออกแบบการทดลอง	การออกแบบการทดลอง
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	Late 1990s–2000s	1940s–1970s (sequential foundations); formalised in clinical and behavioural research by 1980s–2000s
ผู้ริเริ่ม	Developed through convergence of crossover trial methodology (Senn, Williams) and adaptive design methods (Bauer, Köhne, Chow, Chang)	Abraham Wald (sequential analysis foundation); expanded by Robbins, Armitage, and others
ประเภท	Experimental design — hybrid adaptive/crossover	Experimental research design
แหล่งต้นตำรับ	Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman & Hall/CRC. ISBN: 978-1584888468	Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC. ISBN: 978-1584886761
ชื่อเรียกอื่น	adaptive crossover trial, adaptive crossover design, crossover adaptive trial, ACE design	adaptive design, response-adaptive randomization, adaptive trial, adaptive randomization
ที่เกี่ยวข้อง	5	5
สรุป	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.	An adaptive experiment is an experimental design in which pre-specified rules allow the protocol to be modified — such as reallocating participants to better-performing arms, stopping early for efficacy or futility, or changing sample size — based on accumulating interim data, while maintaining statistical validity. Adaptive designs are widely used in clinical trials, behavioural economics, and online platform testing to improve efficiency and ethics without sacrificing inferential rigour.