Clinical trials are the standard approach for evaluating new treatments, but may lack the power to assess rare outcomes. Trial results are also necessarily restricted to the population considered in the study. The availability of routinely collected healthcare data provides a source of information on the performance of treatments beyond that offered by clinical trials, but the analysis of this type of data presents a number of challenges. Hierarchical methods, which take advantage of known relationships between clinical outcomes, while accounting for bias, may be a suitable statistical approach for the analysis of this data. A study of direct oral anticoagulants in Scotland is discussed and used to motivate a modeling approach. A Bayesian hierarchical model, which allows a stratification of the population into clusters with similar characteristics, is proposed and applied to the direct oral anticoagulant study data. A simulation study is used to assess its performance in terms of outcome detection and error rates.

中文翻译：

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针对常规收集的医疗数据中多个结果的贝叶斯分层方法。

临床试验是评估新疗法的标准方法，但可能缺乏评估罕见结果的能力。试验结果也必然限于研究中考虑的人群。常规收集的医疗保健数据的可用性提供了除临床试验所能提供的有关治疗效果的信息来源，但是对此类数据的分析提出了许多挑战。在考虑偏倚的同时利用临床结果之间的已知关系的分层方法可能是用于分析此数据的合适统计方法。讨论了对苏格兰直接口服抗凝药的研究，并将其用于激发建模方法。贝叶斯（Bayesian）分层模型，可将总体分层为具有相似特征的集群，提出并应用于直接口服抗凝研究数据。仿真研究用于评估结果检测和错误率方面的性能。