手法を比較

	適応的ABAデザイン	適応実験
分野	実験計画法	実験計画法
系統	Process / pipeline	Process / pipeline
提唱年	1968 (ABA foundation); adaptive extensions formalized ~2010–2020	1940s–1970s (sequential foundations); formalised in clinical and behavioural research by 1980s–2000s
提唱者	Baer, Wolf & Risley (ABA baseline); adaptive decision-rule extensions developed in single-case methodology literature (Kratochwill & Levin, 2010s)	Abraham Wald (sequential analysis foundation); expanded by Robbins, Armitage, and others
種類	Single-subject experimental design with adaptive phase rules	Experimental research design
原典	Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.	Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC. ISBN: 978-1584886761
別名	adaptive withdrawal design, adaptive ABA withdrawal design, data-driven ABA design, adaptive single-case ABA	adaptive design, response-adaptive randomization, adaptive trial, adaptive randomization
関連	6	5
概要	The Adaptive ABA Design is a single-subject experimental framework that follows the classic three-phase ABA withdrawal structure — baseline (A1), intervention (B), and return-to-baseline (A2) — while embedding prospective decision rules that allow researchers or clinicians to extend, shorten, or otherwise modify each phase in response to observed data patterns rather than following a fixed schedule. This adaptive layer makes the design responsive to individual participant trajectories while preserving experimental control.	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.