Green C E, Moeller F G, Schmitz J M, Lucke J F, Lane S D, Swann A C, Lasky R E, Carbonari J P
Center for Clinical Research & Evidence-Based Medicine, University of Texas, Houston, 77030, USA.
Am J Drug Alcohol Abuse. 2009;35(2):95-102. doi: 10.1080/00952990802647503.
Difficulty identifying effective pharmacotherapies for cocaine dependence has led to suggestions that subgroup differences may account for some of the heterogeneity in treatment response. Well-attested methodological difficulties associated with these analyses recommend the use of Bayesian statistical reasoning for evaluation of salient interaction effects.
A secondary data analysis of a previously published, double-blind, randomized controlled trial examines the interaction of decision-making, as measured by the Iowa Gambling Task, and citalopram in increasing longest sustained abstinence from cocaine use.
Bayesian analysis indicated that there was a 99% chance that improved decision-making enhances response to citalopram. Given the strong positive nature of this finding, a formal, quantitative Bayesian approach to evaluate the result from the perspective of a skeptic was applied.
Bayesian statistical reasoning provides a formal means of weighing evidence for the presence of an interaction in scenarios where conventional, Frequentist analyses may be less informative. [Supplementary materials are available for this article. Go to the publisher's online edition of The American Journal of Drug and Alcohol Abuse for the following free supplemental resource: Appendix 1].
难以确定治疗可卡因依赖的有效药物疗法，这使得有人提出亚组差异可能是治疗反应异质性的部分原因。与这些分析相关的、已得到充分证实的方法学困难，建议使用贝叶斯统计推理来评估显著的交互作用。
对一项先前发表的双盲随机对照试验进行二次数据分析，该试验考察了以爱荷华赌博任务测量的决策能力与西酞普兰在增加可卡因最长持续戒断时间方面的交互作用。
贝叶斯分析表明，决策能力的改善有99%的可能性增强对西酞普兰的反应。鉴于这一发现的强烈阳性性质，应用了一种正式的、定量的贝叶斯方法，从怀疑论者的角度评估结果。
在传统的频率学派分析可能提供的信息较少的情况下，贝叶斯统计推理提供了一种权衡交互作用存在证据的正式方法。[本文提供补充材料。请访问《美国药物与酒精滥用杂志》的出版商在线版本，获取以下免费补充资源：附录1]
