Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Division of General Medicine and Clinical Epidemiology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Diabetes Technol Ther. 2023 Jul;25(7):457-466. doi: 10.1089/dia.2023.0029. Epub 2023 Apr 25.
Randomized trials of continuous glucose monitoring (CGM) often estimate treatment effects using standard intent-to-treat (ITT) analyses. We explored how adjusting for CGM-measured wear time could complement existing analyses by estimating the effect of receiving and using CGM 100% of the time. We analyzed data from two 6-month CGM trials spanning diverse ages, the Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) and CGM Intervention in Teens and Young Adults with Type 1 Diabetes (CITY) Studies. To adjust the ITT estimates for CGM use, as measured by wear time, we used an instrumental variable (IV) approach with the treatment assignment as an instrument. Outcomes included (1) time in range ([TIR] 70-180 mg/dL), time below range ([TBR] ≤70 mg/dL), and time above range ([TAR] ≥250 mg/dL). We estimated outcomes based on CGM use in the last 28 days of the trial and the full trial. In the WISDM study, the wear time rates over the 28-day window and full trial period were 93.1% (standard deviation [SD]: 20.4) and 94.5% (SD: 11.9), respectively. In the CITY study, the wear time rates over the 28-day window and full trial period were 82.2% (SD: 26.5) and 83.1% (SD: 21.5), respectively. IV-based estimates for the effect of CGM on TIR, TBR, and TAR suggested greater improvements in glycemic management than the ITT counterparts. The magnitude of the differences was proportional to the level of wear time observed in the trials. In trials of CGM use, the effect of variable wear time is non-negligible. By providing adherence-adjusted estimates, the IV approach may have additional utility for individual clinical decision-making.
随机对照试验（RCT）常采用标准意向治疗（ITT）分析来评估治疗效果。我们探索了如何通过调整连续血糖监测（CGM）的实际佩戴时间来补充现有分析，从而估计连续 100%使用 CGM 的效果。我们分析了两项为期 6 个月的 CGM 试验的数据，这些试验涵盖了不同年龄段的患者，分别为无线创新治疗老年糖尿病（WISDM）研究和 CGM 干预青少年和年轻成年 1 型糖尿病（CITY）研究。为了根据佩戴时间来调整 ITT 估计值，我们使用了以治疗分配为工具的工具变量（IV）方法。结局包括（1）时间在目标范围内（[TIR]70-180mg/dL）、低于目标范围时间（[TBR]≤70mg/dL）和高于目标范围时间（[TAR]≥250mg/dL）。我们根据试验最后 28 天和整个试验期间的 CGM 使用情况来估计结局。在 WISDM 研究中，28 天窗口和整个试验期间的佩戴时间率分别为 93.1%（标准差 [SD]：20.4）和 94.5%（SD：11.9）。在 CITY 研究中，28 天窗口和整个试验期间的佩戴时间率分别为 82.2%（SD：26.5）和 83.1%（SD：21.5）。基于 IV 的 CGM 对 TIR、TBR 和 TAR 的影响估计提示了血糖管理的更大改善，其效果优于 ITT 分析。差异的幅度与试验中观察到的佩戴时间水平成正比。在 CGM 使用的试验中，可变佩戴时间的影响不容忽视。通过提供依从性调整的估计值，IV 方法可能对个体临床决策具有额外的效用。
