Confounding control in healthcare database research: challenges and potential approaches.
Confounding control in healthcare database research: challenges and potential approaches.
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DOI：
10.1097/mlr.0b013e3181dbebe3
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发表时间：
2010-06
期刊：
影响因子：
3
通讯作者：
Schneeweiss S
中科院分区：
文献类型：
--
作者：
Brookhart MA;Stürmer T;Glynn RJ;Rassen J;Schneeweiss S
流行病学研究越来越多地用于调查医疗产品和干预措施的安全性和有效性。在这类研究中对混杂因素进行适当调整是具有挑战性的，因为暴露是由患者、医生和医疗保健系统因素的复杂相互作用决定的。在使用医疗保健利用数据库的研究中，混淆控制的挑战尤其严重，因为缺乏关于许多潜在混淆因素的信息，而且变量的含义往往不明确。我们讨论了在医疗保健数据库中进行混杂控制的不同方法的优缺点。在数据或治疗分配和结果过程背后的因果机制存在相当大的不确定性的情况下，我们建议研究人员在不同规格的统计模型下报告一组结果。这样的报告允许读者评估结果对模型假设的敏感性，而这些假设通常没有很强的主题知识支持。
Epidemiologic studies are increasingly used to investigate the safety and effectiveness of medical products and interventions. Appropriate adjustment for confounding in such studies is challenging because exposure is determined by a complex interaction of patient, physician, and healthcare system factors. The challenges of confounding control are particularly acute in studies using healthcare utilization databases where information on many potential confounding factors is lacking and the meaning of variables is often unclear. We discuss advantages and disadvantages of different approaches to confounder control in healthcare databases. In settings where considerable uncertainty surrounds the data or the causal mechanisms underlying the treatment assignment and outcome process, we suggest that researchers report a panel of results under various specifications of statistical models. Such reporting allows the reader to assess the sensitivity of the results to model assumptions that are often not supported by strong subject-matter knowledge.