Kelley A Taylor, Incze Michael A, Baumgartner Michael, Campbell Aimee N C, Nunes Edward V, Scharfstein Daniel O
Division of General Internal Medicine, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; Greater Intermountain Node, National Institute on Drug Abuse Clinical Trial Network, Program of Addiction Research, Clinical Care, Knowledge, and Advocacy (PARCKA), Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; Informatics, Decision Enhancement, and Analytic Sciences (IDEAS) Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; Vulnerable Veteran Patient-Aligned Care Team, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
Division of General Internal Medicine, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; Greater Intermountain Node, National Institute on Drug Abuse Clinical Trial Network, Program of Addiction Research, Clinical Care, Knowledge, and Advocacy (PARCKA), Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.
Drug Alcohol Depend. 2024 Aug 1;261:111368. doi: 10.1016/j.drugalcdep.2024.111368. Epub 2024 Jun 12.
High levels of missing outcome data for biologically confirmed substance use (BCSU) threaten the validity of substance use disorder (SUD) clinical trials. Underlying attributes of clinical trials could explain BCSU missingness and identify targets for improved trial design.
We reviewed 21 clinical trials funded by the NIDA National Drug Abuse Treatment Clinical Trials Network (CTN) and published from 2005 to 2018 that examined pharmacologic and psychosocial interventions for SUD. We used configurational analysis-a Boolean algebra approach that identifies an attribute or combination of attributes predictive of an outcome-to identify trial design features and participant characteristics associated with high levels of BCSU missingness. Associations were identified by configuration complexity, consistency, coverage, and robustness. We limited results using a consistency threshold of 0.75 and summarized model fit using the product of consistency and coverage.
For trial design features, the final solution consisted of two pathways: psychosocial treatment as a trial intervention OR larger trial arm size (complexity=2, consistency=0.79, coverage=0.93, robustness score=0.71). For participant characteristics, the final solution consisted of two pathways: interventions targeting individuals with poly- or nonspecific substance use OR younger age (complexity=2, consistency=0.75, coverage=0.86, robustness score=1.00).
Psychosocial treatments, larger trial arm size, interventions targeting individuals with poly- or nonspecific substance use, and younger age among trial participants were predictive of missing BCSU data in SUD clinical trials. Interventions to mitigate missing data that focus on these attributes may reduce threats to validity and improve utility of SUD clinical trials.
生物学确诊的物质使用（BCSU）结果数据大量缺失，威胁着物质使用障碍（SUD）临床试验的有效性。临床试验的潜在属性可以解释BCSU数据缺失的原因，并确定改进试验设计的目标。
我们回顾了2005年至2018年期间由美国国家药物滥用研究所国家药物滥用治疗临床试验网络（CTN）资助并发表的21项临床试验，这些试验研究了针对SUD的药物和心理社会干预措施。我们使用构型分析——一种布尔代数方法，可识别预测结果的属性或属性组合——来确定与高水平BCSU数据缺失相关的试验设计特征和参与者特征。通过构型复杂性、一致性、覆盖率和稳健性来确定关联。我们使用0.75的一致性阈值来限制结果，并使用一致性和覆盖率的乘积来总结模型拟合情况。
对于试验设计特征，最终解决方案包括两条途径：心理社会治疗作为试验干预措施或更大的试验组规模（复杂性=2，一致性=0.79，覆盖率=0.93，稳健性得分=0.71）。对于参与者特征，最终解决方案包括两条途径：针对多种或非特定物质使用个体的干预措施或较年轻的年龄（复杂性=2，一致性=0.75，覆盖率=0.86，稳健性得分=1.00）。
心理社会治疗、更大的试验组规模、针对多种或非特定物质使用个体的干预措施以及试验参与者较年轻的年龄，可预测SUD临床试验中BCSU数据的缺失。针对这些属性的减少缺失数据的干预措施，可能会降低对有效性的威胁，并提高SUD临床试验的效用。
