Tong Guangyu, Coronado Gloria D, Li Chenxi, Li Fan
Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA; Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA; Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, CT, USA.
College of Public Health, The University of Arizona, Tucson, AZ, USA.
Contemp Clin Trials. 2025 Jan;148:107764. doi: 10.1016/j.cct.2024.107764. Epub 2024 Nov 26.
Pragmatic trials that combine electronic health record data and patient-reported data may be subject to selection bias due to the differential post-randomization exclusion of participants who are randomized in error. Such situations are often caused by inevitable reasons, such as incomplete patient medical records at the pre-randomization stage. This can lead to participants in the intervention arm being identified as ineligible after randomization, while randomized-in-error participants in the usual care are often not discernable. The differential exclusion can present analytic challenges and threaten result validity.
Under the potential outcomes framework, we developed a Bayesian model that jointly identifies the randomized-in-error status and estimates the average treatment effect among participants not randomized in error. We designed simulation studies with hypothesized proportions of 5 %-15 % randomization in error to evaluate the performance of our model across scenarios where the outcomes of participants randomized in error were either measured or unmeasured. Comparisons were made to intention-to-treat and covariate-adjusted estimators.
Simulation results show satisfactory performance of our proposed models, where the estimated average treatment effects among participants not randomized in error have low bias (<1 %) and close to 95 % coverage. Estimates from the alternative approaches can exhibit notable biases and low coverage.
Differential exclusion in pragmatic clinical trials after randomization can lead to selection bias. Under certain assumptions, Bayesian methods provide a feasible solution to jointly identify randomized-in-error status and estimate the average treatment effect among participants not randomized in error, ensuring more reliable and valid inferences about intervention effects.
结合电子健康记录数据和患者报告数据的实用试验可能会因错误随机分组的参与者在随机化后被不同程度地排除而受到选择偏倚的影响。这种情况通常是由不可避免的原因导致的，比如随机化前阶段患者病历不完整。这可能导致干预组的参与者在随机化后被认定为不符合条件，而常规护理组中随机分组错误的参与者往往难以辨别。这种不同程度的排除会带来分析上的挑战，并威胁结果的有效性。
在潜在结果框架下，我们开发了一种贝叶斯模型，该模型能共同识别随机分组错误状态，并估计未随机分组错误的参与者的平均治疗效果。我们设计了模拟研究，假设错误随机分组的比例为5%-15%，以评估我们的模型在错误随机分组参与者的结果被测量或未被测量的各种情况下的性能。并与意向性分析和协变量调整估计方法进行了比较。
模拟结果显示我们提出的模型性能令人满意，其中未随机分组错误的参与者的估计平均治疗效果偏差较低（<1%），覆盖率接近95%。替代方法的估计可能会出现明显偏差和低覆盖率。
实用临床试验随机化后的不同程度排除可能导致选择偏倚。在某些假设下，贝叶斯方法为共同识别随机分组错误状态和估计未随机分组错误的参与者的平均治疗效果提供了一种可行的解决方案，确保对干预效果的推断更加可靠和有效。
