Design and analysis advances to improve generalizability of clinical trials for treating opioid use disorder

美国的阿片类药物流行是一种公共卫生紧急情况，并因新冠肺炎大流行而加剧。阿片类药物使用障碍（MOUD）-注射纳洛酮，丁丙诺啡和美沙酮-是改善OUD患者结局和预防药物过量的最有效工具，但参与MOUD，特别是成功结局通常所需的长期参与，是不可接受的低。在现实世界中，长期参与率往往更低-NIDA称之为研究与实践的差距。试验和真实世界MOUD有效性之间的这种差异可能部分归因于临床试验与真实世界人群特征之间的差异（例如，在精神病和药物使用共病、既往治疗经验、移民身份等方面）如果治疗效果因其中一些也与试验参与相关的特征而改变（增加/降低）。此外，在不知道MOUD对某些现实世界目标人群的相对有效性的情况下，临床医生、研究人员和政策制定者可能会被要求使用有偏见的证据进行决策。因此，迫切需要提高MOUD试验的普遍性。如果不能满足这一需求，将进一步巩固研究与实践之间的差距，导致整体和关键亚组内的OUD治疗效果不佳。我们建议开发设计和分析方法，我们称之为通过线的概括性，桥梁MOUD试验证据，以现实世界的人口。本项目的目标是：在目标1）中，识别和描述在美国常规护理环境中寻求OUD治疗的具有临床意义、可解释的亚组，这些亚组在MOUD试验中未代表或代表性不足，同时基于多种特征。这将使我们超越现有的评估代表性的方法，这些方法通常仅限于一次考虑一个个体水平的特征（例如，种族/民族）。我们将把目标1第一部分中开发的方法应用于试验数据（来自NIDA CTN的3项MOUD试验）和人口数据（加州和新泽西医疗补助申请），以表征代表性不足的亚组。在目标2）中，将MOUD有效性推广到特定州的成年医疗补助人群，从而估计一个现实的治疗目标，如果治疗保留支持，激励措施和给药实践得到改进，以与试验中的那些保持一致。现有的预测广义效应的方法依赖于对未代表和代表性不足的亚组的外推，这可能导致有偏倚和/或无信息的估计。在目标2的第一部分中开发的方法将进行几项改进，以限制外推并提高效率。在目标3）中，在用户友好的软件中实施为目标1和2开发的方法，以促进应用试验者、研究人员和临床医生的轻松采用。拟议中的研究预计将作出重大贡献，以提高代表性之间的试验参与者，并了解如何和谁的试验结果概括。

The opioid epidemic in the US is a public health emergency, exacerbated by the Covid-19 pandemic. Medi- cations for opioid use disorder (MOUD)-injection naltrexone, buprenorphine, and methadone-are the most effective tools for improving outcomes and preventing overdose among persons with OUD, but engagement in MOUD, especially long-term engagement typically required for a successful outcome, is unacceptably low. Long-term engagement rates tend to be even lower in real-world settings-what NIDA has termed the research-to-practice gap. This discrepancy between trial and real-world MOUD effectiveness could be par- tially attributable to differences between clinical trial versus real-world population characteristics (e.g., in terms of psychiatric and substance use comorbidities, previous treatment experience, immigration status, etc.) if treatment effects are modified (increased/decreased) by some of these characteristics that also relate to trial participation. Moreover, without knowing the relative effectiveness of MOUDs for certain real-world target pop- ulations, clinicians, researchers, and policymakers may be tasked with decision-making with biased evidence. Thus, there is a critical need to improve the generalizability of MOUD trials. Failing to meet this need would further ossify the research-to-practice gap, resulting in suboptimal treatment of OUD overall and within key subgroups. We propose to develop design and analytic approaches, what we call a generalizability through- line, to bridge MOUD trial evidence to real-world populations. The objectives of this project are: In Aim 1), to identify and characterize clinically meaningful, interpretable subgroups of persons seeking OUD treatment in US usual-care settings who are not represented or under-represented in MOUD trials based on multiple char- acteristics simultaneously. This will move us beyond existing approaches for assessing representation that have generally been limited to considering one individual-level characteristic at a time (e.g., race/ethnicity). We will apply the approach developed in the first part of Aim 1 to trial data (3 MOUD trials from NIDA CTN) and population data (California and New Jersey Medicaid claims) to characterize under-represented subgroups. In Aim 2), to generalize MOUD effectiveness to state-specific adult Medicaid populations, thereby estimating a realistic treatment goal if treatment retention supports, incentives, and dosing practices were improved to align with those in trials. Existing approaches for predicting generalized effects rely on extrapolation for non- and under-represented subgroups, which can result in biased and/or uninformative estimates. The approach developed in the first part of Aim 2 will make several improvements to limit extrapolation and increase effi- ciency. In Aim 3), to implement the methods developed for Aims 1 and 2 in user-friendly software to facilitate the easy adoption by applied trialists, researchers, and clinicians. The proposed research is expected to make a significant contribution to improving representation among trial participants and to understanding how and to whom trial findings generalize.