Quality Use of Medicines and Pharmacy Research Centre (QUMPRC), UniSA Clinical and Health Sciences, CEA-19, University of South Australia, Adelaide, SA, 5001, Australia.
School of Rural Health, Monash University, 26 Mercy Street, Bendigo, VIC, 3550, Australia.
BMC Med Res Methodol. 2021 Feb 27;21(1):43. doi: 10.1186/s12874-021-01230-z.
The case-crossover design is suited to medication safety studies but is vulnerable to exposure misclassification. Using the example of tricyclic antidepressants and the risk of hip fracture, we present a data visualisation tool for observing exposure misclassification in case-crossover studies.
A case-crossover study was conducted using Australian Government Department of Veterans' Affairs claims data. Beneficiaries aged over 65 years who were hospitalised for hip fracture between 2009 and 2012 were included. The case window was defined as 1-50 days pre fracture. Control window one and control window two were defined as 101-150 and 151-200 days pre fracture, respectively. Patients were stratified by whether exposure status changed when control window two was specified instead of control window one. To visualise potential misclassification, each subject's tricyclic antidepressant dispensings were plotted over the 200 days pre fracture.
The study population comprised 8828 patients with a median age of 88 years. Of these subjects, 348 contributed data to the analyses with either control window. The data visualisation suggested that 14% of subjects were potentially misclassified with control window one while 45% were misclassified with control window two. The odds ratio for the association between tricyclic antidepressants and hip fracture was 1.18 (95% confidence interval = 0.91-1.52) using control window one, whereas risk was significantly increased (odds ratio = 1.43, 95% confidence interval = 1.11-1.83) using control window two.
Exposure misclassification was less likely to be present with control window one than with an earlier control window, control window two. When specifying different control windows in a case-crossover study, data visualisation can help to assess the extent to which exposure misclassification may contribute to variable results.
病例交叉设计适用于药物安全性研究，但易发生暴露错误分类。我们以三环类抗抑郁药与髋部骨折风险为例，介绍一种用于观察病例交叉研究中暴露错误分类的数据可视化工具。
使用澳大利亚退伍事务部的索赔数据进行病例交叉研究。纳入 2009 年至 2012 年期间因髋部骨折住院的年龄在 65 岁以上的患者。病例窗口定义为骨折前 1-50 天。将控制窗口 1 和控制窗口 2 分别定义为骨折前 101-150 天和 151-200 天。根据当将控制窗口 2 而不是控制窗口 1 指定为对照时，暴露状态是否发生变化，对患者进行分层。为了可视化潜在的错误分类，将每位患者的三环类抗抑郁药配药情况绘制在骨折前 200 天内。
研究人群包括 8828 名中位年龄为 88 岁的患者。其中 348 名患者有 1 个或 2 个控制窗口的分析数据。数据可视化显示，14%的患者在使用控制窗口 1 时可能存在错误分类，而 45%的患者在使用控制窗口 2 时存在错误分类。使用控制窗口 1 时，三环类抗抑郁药与髋部骨折之间的关联比值比为 1.18（95%置信区间 0.91-1.52），而使用控制窗口 2 时，风险显著增加（比值比 1.43，95%置信区间 1.11-1.83）。
与更早的控制窗口（控制窗口 2）相比，使用控制窗口 1 时，暴露错误分类的可能性更小。在病例交叉研究中指定不同的控制窗口时，数据可视化可帮助评估暴露错误分类可能导致结果变量的程度。
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