标题： 使用外部对照的混合随机对照试验中的选择偏差：一项模拟研究 显示英文标题

标题： Selection Bias in Hybrid Randomized Controlled Trials using External Controls: A Simulation Study

摘要： 混合随机对照试验（混合RCT）将外部对照数据，如历史数据或同时期数据，与随机试验的数据结合起来。 虽然已经开发了许多频率学派和贝叶斯方法，例如检验后合并和元分析预测先验，以考虑外部对照数据和随机数据之间可能出现的不一致，但它们不能确保严格的I型错误率控制。 然而，这些方法可以减少由于外部对照和试验数据之间的系统性差异引起的偏差。 混合RCT中的一个关键但研究不足的问题是预先指定用于分析的外部数据。 混合RCT中统计结论的有效性依赖于这样一个假设，即外部对照的选择独立于历史试验的结果。 在实践中，历史数据可能在规划阶段就可获得，这可能会对重要决策产生影响，例如选择哪些历史数据集或混合试验的前瞻性部分的样本量，从而引入偏差。 这种数据驱动的设计选择可能是额外的偏差来源，即使历史和前瞻性对照是可交换的，这种偏差也可能出现。 通过一项模拟研究，我们使用贝叶斯和频率学派方法量化了在混合RCT中因结果依赖性选择历史对照而引入的偏差，并讨论了减轻这种偏差的潜在策略。 我们的场景考虑了历史研究中的变异性及时间趋势、历史与前瞻性对照组之间的分布变化、样本量和分配比例以及所包含的研究数量。 通过一个临床试验的例子展示了选择外部对照的不同规则的影响。 显示英文摘要

摘要： Hybrid randomized controlled trials (hybrid RCTs) integrate external control data, such as historical or concurrent data, with data from randomized trials. While numerous frequentist and Bayesian methods, such as the test-then-pool and Meta-Analytic-Predictive prior, have been developed to account for potential disagreement between the external control and randomized data, they cannot ensure strict type I error rate control. However, these methods can reduce biases stemming from systematic differences between external controls and trial data. A critical yet underexplored issue in hybrid RCTs is the prespecification of external data to be used in analysis. The validity of statistical conclusions in hybrid RCTs depends on the assumption that external control selection is independent of historical trials outcomes. In practice, historical data may be accessible during the planning stage, potentially influencing important decisions, such as which historical datasets to include or the sample size of the prospective part of the hybrid trial, thus introducing bias. Such data-driven design choices can be an additional source of bias, which can occur even when historical and prospective controls are exchangeable. Through a simulation study, we quantify the biases introduced by outcome-dependent selection of historical controls in hybrid RCTs using both Bayesian and frequentist approaches, and discuss potential strategies to mitigate this bias. Our scenarios consider variability and time trends in the historical studies, distributional shifts between historical and prospective control groups, sample sizes and allocation ratios, as well as the number of studies included. The impact of different rules for selecting external controls is demonstrated using a clinical trial example.