BACKGROUND Pragmatic trials provide the opportunity to study the effectiveness of health interventions to improve care in real-world settings. However, use of open-cohort designs with patients becoming eligible after randomization and reliance on electronic health records (EHRs) to identify participants may lead to a form of selection bias referred to as identification bias. This bias can occur when individuals identified as a result of the treatment group assignment are included in analyses. METHODS To demonstrate the importance of identification bias and how it can be addressed, we consider a motivating case study, the PRimary care Opioid Use Disorders treatment (PROUD) Trial. PROUD is an ongoing pragmatic, cluster-randomized implementation trial in six health systems to evaluate a program for increasing medication treatment of opioid use disorders (OUDs). A main study objective is to evaluate whether the PROUD intervention decreases acute care utilization among patients with OUD (effectiveness aim). Identification bias is a particular concern, because OUD is underdiagnosed in the EHR at baseline, and because the intervention is expected to increase OUD diagnosis among current patients and attract new patients with OUD to the intervention site. We propose a framework for addressing this source of bias in the statistical design and analysis. RESULTS The statistical design sought to balance the competing goals of fully capturing intervention effects and mitigating identification bias, while maximizing power. For the primary analysis of the effectiveness aim, identification bias was avoided by defining the study sample using pre-randomization data (pre-trial modeling demonstrated that the optimal approach was to use individuals with a prior OUD diagnosis). To expand generalizability of study findings, secondary analyses were planned that also included patients newly diagnosed post-randomization, with analytic methods to account for identification bias. CONCLUSION As more studies seek to leverage existing data sources, such as EHRs, to make clinical trials more affordable and generalizable and to apply novel open-cohort study designs, the potential for identification bias is likely to become increasingly common. This case study highlights how this bias can be addressed in the statistical study design and analysis. TRIAL REGISTRATION ClinicalTrials.gov, NCT03407638. Registered on 23 January 2018.

中文翻译：

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在设计和分析集群随机语用试验时解决识别偏见：一个案例研究。

背景技术实用试验提供了机会来研究健康干预措施以改善现实环境中的护理的有效性。但是，在患者经过随机分组并依靠电子健康记录（EHR）来识别参与者后才有资格使用开放式队列设计可能会导致一种选择偏见，称为识别偏见。当分析中包括因治疗组分配而确定的个体时，可能会发生这种偏见。方法为了证明识别偏倚的重要性及其解决方法，我们考虑了一个动机案例研究，即初级保健阿片类药物使用障碍治疗（PROUD）试验。PROUD持续务实，在六个卫生系统中进行集群随机实施试验，以评估增加阿片类药物使用障碍（OUD）药物治疗的计划。主要研究目标是评估PROUD干预措施是否会降低OUD患者的急性护理利用率（有效性目标）。识别偏倚是一个特别令人担忧的问题，因为在基线时EHR中OUD的诊断不足，并且由于该干预措施有望增加当前患者中的OUD诊断，并将新的OUD患者吸引到干预部位。我们提出了一个框架来解决统计设计和分析中的这种偏见。结果统计设计试图在充分利用干预效果和减轻识别偏差的同时平衡竞争目标，同时使能力最大化。对于有效性目标的初步分析，通过使用预先随机化的数据定义研究样本来避免识别偏倚（预先模拟表明，最佳方法是使用事先进行过OUD诊断的个体）。为了扩大研究结果的可推广性，计划进行次要分析，其中还包括新诊断为随机后的患者，并采用分析方法来解决识别偏差。结论随着越来越多的研究试图利用现有数据源（例如EHR），使临床试验的价格更加可负担且可推广，并应用新颖的开放队列研究设计，识别偏倚的可能性可能会越来越普遍。本案例研究强调了如何在统计研究设计和分析中解决这种偏见。试验注册临床试验。政府，NCT03407638。2018年1月23日注册。