Missing Data Matters: Substance Use Disorder Clinical Trials

缺失数据很重要：药物使用障碍临床试验

• 批准号: 9923614
• 负责人: Daniel Oscar Scharfstein
• 金额: $ 10.03万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9923614
• 关键词: Address; Adoption; Alcohol or Other Drugs use; Case Study; Clinical; Clinical Trials; Clinical Trials Network; Communities; Computer software; Conduct Clinical Trials; Coupled; Data; Data Analyses; Data Set; Development; Ensure; Evaluation Studies; Explosion; Future; Goals; HIV; HIV risk; Health; Health system; Individual; Intervention Studies; Lead; Link; Literature; Methodology; Methods; Movement; National Institute of Drug Abuse; Online Systems; Outcome; Outcome Assessment; Outcome Measure; Participant; Pattern; Performance; Persons; Pharmaceutical Preparations; Principal Investigator; Procedures; Property; Publishing; Randomized; Randomized Clinical Trials; Reporting; Reproducibility; Research; Research Design; Research Personnel; Risk Behaviors; Schedule; Scientist; Software Tools; Source; Stress Tests; Substance Use Disorder; Substance abuse problem; Testing; Time; Uncertainty; Urine; Withdrawal; Work; addiction; base; clinical trial analysis; data sharing; flexibility; innovation; interest; journal article; knowledge translation; machine learning algorithm; novel; open data; open source; premature; prevent; programs; secondary analysis; simulation; software development; study characteristics; tool; treatment effect; user friendly software; user-friendly; web site; webinar

Program Director/Principal Investigator (Last, First, Middle): Scharfstein, Daniel, Oscar Project Summary/Abstract Missing outcome data threaten the validity of randomized clinical trials because inference about treatment effects then necessarily relies on untestable assumptions, which wrongly stated can lead to incorrect conclusions. While it is widely recognized that evaluating the sensitivity of trial results to assumptions about the missing data mech- anism should be a mandatory component of reporting, rigorous sensitivity analyses are not routinely reported. Likely explanations include inadequate knowledge translation by statistical methodologists to both principal in- vestigators and their statistical collaborators as well as lack of software. Substance use disorder clinical trials are known to suffer from high rates of missing data. Unlike regulatory trials where missing data are primarily the result of premature study withdrawal, individuals in substance use disorder trials tend to intermittently skip their scheduled outcome assessments. This produces an explosion of “non-monotone” missing data patterns that makes sensitivity analysis methodologically and computationally chal- lenging. There has been relatively little research on sensitivity analysis procedures for analyzing such data and the procedures that have been developed are anchored to assumptions that are problematic. Thus, investigators are faced with challenging analytic barriers and the conclusions they draw from their trials may be flawed. In this three-year proposal, we will reanalyze 29 clinical trials conducted by NIDA's Clinical Trials Network (CTN), and made publicly available on NIDA's DataShare website, to evaluate their robustness to missing data assump- tions through rigorous sensitivity analysis. Since adequate tools for conducting sensitivity analysis of studies with highly non-monotone missing data patterns do not yet exist, we plan to develop, implement and dissemi- nate (through journal articles, short courses and webinars) an innovative sensitivity analysis methodology and open-source, user-friendly software to evaluate the robustness, to missing data assumptions, of trials in which binary outcomes (e.g., substance use) are scheduled to be repeatedly collected at fixed points in time after ran- domization and participants intermittently skip their scheduled assessments. Our tool will be developed by an interdisciplinary team of biostatisticians and substance use disorder treatment experts, with input from an advi- sory board comprised of highly regarded statistical experts and leading scientists in the substance use disorder community. Through reanalysis of the NIDA's CTN trials using our tool, we will be better able to understand the impact of missing data assumptions on the evaluation of the studied interventions. Additionally, demonstrat- ing the importance and utility of our tool to our advisory board and to the substance use disorder community more broadly stands to increase the likelihood of adoption. Finally, the development, testing, and dissemination of this innovative statistical tool can serve as a template for other scientific domains, making “stress-testing” to untestable missing data assumptions a more routine component of scientific reporting.

项目负责人/主要研究者（最后，第一，中间）：Scharfstein，丹尼尔，奥斯卡 项目总结/摘要 由于对治疗效果的推断， 然后必然依赖于不可检验的假设，错误的陈述可能导致错误的结论。而 人们普遍认为，评估试验结果对缺失数据机制假设的敏感性， 非敏感性分析应是报告的一个强制性组成部分，严格的敏感性分析并不是例行报告。 可能的解释包括统计方法学家对两个主要领域的知识翻译不足- 调查人员及其统计合作者以及缺乏软件。 物质使用障碍临床试验是众所周知的遭受高比率的数据缺失。不同于监管 缺失数据主要是由于提前退出研究、使用药物的个人 疾病试验往往会间歇性地跳过预定的结果评估。这就产生了一个爆炸， “非单调”缺失数据模式使得敏感性分析在方法和计算上具有挑战性， 我是说，关于分析这些数据的敏感性分析程序的研究相对较少， 已经制定的程序所依据的假设是有问题的。因此，调查人员 面临着具有挑战性的分析障碍，他们从试验中得出的结论可能会受到质疑。 在这个为期三年的提案中，我们将重新分析NIDA临床试验网络（CTN）进行的29项临床试验， 并在NIDA的DataShare网站上公开发布，以评估其对缺失数据的鲁棒性。 通过严格的敏感性分析。由于有足够的工具对研究进行敏感性分析， 由于尚不存在高度非单调的缺失数据模式，我们计划开发、实施和传播- nate（通过期刊文章，短期课程和网络研讨会）创新的敏感性分析方法， 开源、用户友好的软件，用于评估试验对缺失数据假设的稳健性， 二元结果（例如，（物质使用）计划在运行后的固定时间点重复收集， 圆顶化和参与者间歇性地跳过他们预定的评估。我们的工具将由一个 生物统计学家和物质使用障碍治疗专家的跨学科团队， 一个由高度重视的统计专家和物质使用障碍方面的领先科学家组成的委员会 社区通过使用我们的工具重新分析NIDA的CTN试验，我们将能够更好地理解 缺失数据假设对所研究干预措施评价的影响。此外，示范- 将我们的工具的重要性和实用性介绍给我们的咨询委员会和物质使用障碍社区 更广泛地增加了采用的可能性。最后，开发、测试和传播 这种创新的统计工具可以作为其他科学领域的模板，使“压力测试”， 不可检验的缺失数据假设是科学报告中更常规的组成部分。