Single-arm Trials With External Comparators and Confounder Misclassification: How Adjustment Can Fail.
Single-arm Trials With External Comparators and Confounder Misclassification: How Adjustment Can Fail.
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DOI：
10.1097/mlr.0000000000001400
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发表时间：
2020-12
期刊：
影响因子：
3
通讯作者：
Stürmer T
中科院分区：
文献类型：
--
作者：
Webster-Clark M;Jonsson Funk M;Stürmer T
使用外部对照药物的“单臂试验”，将实验性治疗的结果与现实世界患者的结果进行对比，已用于评估实验性药物在罕见和严重疾病中的疗效和安全性。监管机构正在考虑扩大这些研究可以发挥的作用;到目前为止，指南明确考虑了结果错误分类，很少讨论混淆变量的错误分类。这项工作使用因果图来说明如何调整一个错误分类的混杂因素可能会导致估计远离真相，而不是完全忽略它。这一理论是增强了定量的例子，使用合理的值在现实世界中的药物索赔数据的错误分类吸烟。还提供了一个工具，用于计算具有特定输入参数的调整后估计值的偏差。当两个数据源的混淆因素误分类相似时，调整通常会使估计值更接近真实值。如果不是这样，调整后的估计可能会比粗略的估计更偏离事实。虽然所有非随机研究都存在这种潜在偏倚，但由于混淆测量和治疗组的完美对齐，单组研究尤其容易受到影响。当数据源之间混淆因素的流行率没有差异且错误分类确实存在差异时，这是最成问题的，但即使混淆因素与数据源之间存在很强的关联，也可能发生这种情况。研究者在设计这类研究的方案时，应考虑混杂因素的分类错误。混杂因素的子样本验证，随后是插补或其他偏倚校正方法，可能是未来结合试验和真实世界数据的关键工具。
“Single-arm trials” with external comparators that contrast outcomes in those on experimental therapy to real-world patients have been used to evaluate efficacy and safety of experimental drugs in rare and severe diseases. Regulatory agencies are considering expanding the role these studies can play; guidance thus far has explicitly considered outcome misclassification with little discussion of misclassification of confounding variables. This work uses causal diagrams to illustrate how adjustment for a misclassified confounder can result in estimates farther from the truth than ignoring it completely. This theory is augmented with quantitative examples using plausible values for misclassification of smoking in real-world pharmaceutical claims data. A tool is also provided for calculating bias of adjusted estimates with specific input parameters. When confounder misclassification is similar in both data sources, adjustment generally brings estimates closer to the truth. When it is not, adjustment can generate estimates that are considerably farther from the truth than the crude. While all non-randomized studies are subject to this potential bias, single-arm studies are particularly vulnerable due to perfect alignment of confounder measurement and treatment group. This is most problematic when the prevalence of the confounder does not differ between data sources and misclassification does, but can occur even with strong confounder-data source associations. Researchers should consider differential confounder misclassification when designing protocols for these types of studies. Subsample validation of confounders, followed by imputation or other bias correction methods, may be a key tool for combining trial and real-world data going forward.