Brown Alexandra R, Gajewski Byron J, Mudaranthakam Dinesh Pal, Pasnoor Mamatha, Dimachkie Mazen M, Jawdat Omar, Herbelin Laura, Mayo Matthew S, Barohn Richard J
Department of Biostatistics & Data Science, The University of Kansas Medical Center, Kansas City, KS, USA.
Department of Neurology, The University of Kansas Medical Center, Kansas City, KS, USA.
Contemp Clin Trials Commun. 2023 Oct 14;36:101220. doi: 10.1016/j.conctc.2023.101220. eCollection 2023 Dec.
Response adaptive randomization is popular in adaptive trial designs, but the literature detailing its execution is lacking. These designs are desirable for patients/stakeholders, particularly in comparative effectiveness research, due to the potential benefits including improving participant buy-in by providing more participants with better treatment during the trial. Frequentist approaches have often been used, but adaptive designs naturally fit the Bayesian methodology; it was developed to deal with data as they come in by updating prior information.
PAIN-CONTRoLS was a comparative-effectiveness trial utilizing Bayesian response adaptive randomization to four drugs, nortriptyline, duloxetine, pregabalin, or mexiline, for cryptogenic sensory polyneuropathy (CSPN) patients. The aim was to determine which treatment was most tolerable and effective in reducing pain. Quit and efficacy rates were combined into a utility function to develop a single outcome, which with treatment sample size, drove the adaptive randomization. Prespecified interim analyses allowed the study to stop for early success or update the randomization probabilities to the better-performing treatments.
Seven adaptations to the randomization occurred before the trial ended due to reaching the maximum sample size, with more participants receiving nortriptyline and duloxetine. At the end of the follow-up, nortriptyline and duloxetine had lower probabilities of participants that had stopped taking the study medication and higher probabilities were efficacious. Mexiletine had the highest quit rate, but had an efficacy rate higher than pregabalin.
Response adaptive randomization has become a popular trial tool, especially for those utilizing Bayesian methods for analyses. By illustrating the execution of a Bayesian adaptive design, using the PAIN-CONTRoLS trial data, this paper continues the work to provide literature for conducting Bayesian response adaptive randomized trials.
响应自适应随机化在适应性试验设计中很常见，但缺乏详细描述其实施过程的文献。这些设计对患者/利益相关者来说很有吸引力，特别是在比较效果研究中，因为其潜在益处包括在试验期间为更多参与者提供更好的治疗，从而提高参与者的参与度。虽然经常采用频率论方法，但适应性设计自然适合贝叶斯方法；贝叶斯方法是为了在数据输入时通过更新先验信息来处理数据而开发的。
PAIN-CONTRoLS是一项比较效果试验，采用贝叶斯响应自适应随机化方法，将四种药物（去甲替林、度洛西汀、普瑞巴林或美西律）用于隐源性感觉性多发性神经病（CSPN）患者。目的是确定哪种治疗在减轻疼痛方面最耐受且有效。将退出率和有效率合并到一个效用函数中以得出单一结果，该结果与治疗样本量一起驱动自适应随机化。预先设定的中期分析允许研究因早期成功而停止，或更新为表现更好的治疗的随机化概率。
由于达到最大样本量，在试验结束前对随机化进行了七次调整，更多参与者接受了去甲替林和度洛西汀。在随访结束时，去甲替林和度洛西汀组中停止服用研究药物的参与者概率较低，而有效的概率较高。美西律的退出率最高，但其有效率高于普瑞巴林。
响应自适应随机化已成为一种流行的试验工具，特别是对于那些使用贝叶斯方法进行分析的研究。通过使用PAIN-CONTRoLS试验数据说明贝叶斯自适应设计的实施过程，本文继续为进行贝叶斯响应自适应随机试验提供文献资料。
