Missing Data Matters: Substance Use Disorder Clinical Trials

项目总监/首席调查员(最后、第一、中间)：沙尔夫斯坦、丹尼尔、奥斯卡 项目摘要/摘要 缺失的结果数据威胁着随机临床试验的有效性，因为对治疗效果的推断 那么必然依赖于不可检验的假设，错误的陈述可能会导致错误的结论。而当 人们普遍认为，评估试验结果对关于缺失数据机制的假设的敏感性-- 作为报告的强制性组成部分，严谨的敏感性分析并不是常规报告。 可能的解释包括统计方法学家对这两个主要问题的知识翻译不足。 调查员和他们的统计合作者以及缺乏软件。 众所周知，物质使用障碍临床试验存在数据缺失率高的问题。与监管机构不同 试验中，数据丢失主要是由于过早退出研究，个人在物质使用方面 障碍试验往往间歇性地跳过他们预定的结果评估。这就产生了爆炸式的 “非单调”缺失数据模式，使得敏感度分析在方法和计算上都有困难-- 拉长。对用于分析此类数据的敏感性分析程序的研究相对较少 已经制定的程序是以有问题的假设为基础的。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是fl敬畏的。 在这个为期三年的提案中，我们将重新分析由NIDA的临床试验网络(CTN)进行的29项临床试验， 并在NIDA的DataShare网站上公开提供，以评估它们对丢失数据假设的稳健性- 通过严格的敏感性分析。由于有足够的工具来进行研究的敏感性分析 由于目前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和 开源、用户友好的软件，用于评估以下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fix时间点重复收集- 控制力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 由生物统计学家和物质使用障碍治疗专家组成的跨学科团队，来自一项建议- 由备受尊敬的统计专家和物质使用障碍领域的顶尖科学家组成的Sory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估的影响。此外，示威者- 让我们的工具对我们的顾问委员会和药物使用障碍社区的重要性和实用性 更广泛地说，这将增加被采用的可能性。最后，开发、测试和传播 这一创新的统计工具可以作为其他Sciencefic域的模板，进行“压力测试”以 不可测试的缺失数据假设是Sciencefic报告的一个更常规的组成部分。

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.