Meid Andreas D, Heider Dirk, Adler Jürgen-Bernhard, Quinzler Renate, Brenner Herrmann, Günster Christian, König Hans-Helmut, Haefeli Walter E
Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany.
Department of Health Economics and Health Services Research, Hamburg Center for Health Economics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Pharmacoepidemiol Drug Saf. 2016 Dec;25(12):1434-1442. doi: 10.1002/pds.4091. Epub 2016 Sep 16.
The purpose of this study was to compare the predictive accuracy of different methods suggested for approximation of drug prescription durations in claims data.
We expanded a well-established modeling and simulation framework to compare approximated drug prescription durations with 'true' (i.e., simulated) durations. Real claims data of persons aged ≥65 years insured by the German nationwide 'Statutory Health Insurance Fund' AOK between 2010 and 2012 provided empiric input parameters that were completed with missing information on actual dosing patterns from an observational cohort. The distinct approximation methods were based on crude measures (one tablet a day), population-averaged measures (defined daily doses), or individually-derived measures (longitudinal coverage approximation of the applied dose, COV). As a proof-of-principle, we assessed the methods' performance to predict the well-characterized bleeding risks of anticoagulant, antiplatelet, and/or non-steroidal anti-inflammatory drugs.
When applied to modeling and simulation data sets, the closest, least biased, and thus most accurate approximation was observed using the COV approximation. In a real-data example, rather similar results to an external reference were obtained for all methods. However, some of the differences between methods were meaningful, and the most reasonable and consistent results were obtained with the COV approach.
Based on theoretically most accurate approximations and practically reasonable estimates, the individual COV approach was preferable over the population-averaged defined daily dose technique, although the latter might be justified in certain situations. Advantages of the COV approach are expected to be even bigger for drug therapies with particularly large dosing heterogeneity. Copyright © 2016 John Wiley & Sons, Ltd.
本研究旨在比较索赔数据中用于估算药物处方持续时间的不同方法的预测准确性。
我们扩展了一个成熟的建模与模拟框架，以比较估算的药物处方持续时间与“真实”（即模拟）持续时间。2010年至2012年期间，由德国全国性的“法定医疗保险基金”AOK承保的65岁及以上人群的真实索赔数据提供了经验输入参数，并通过观察队列中实际给药模式的缺失信息进行了补充。不同的估算方法基于粗略测量（每日一片）、人群平均测量（限定日剂量）或个体衍生测量（应用剂量的纵向覆盖估算，COV）。作为原理验证，我们评估了这些方法预测抗凝药、抗血小板药和/或非甾体抗炎药明确的出血风险的性能。
当应用于建模和模拟数据集时，使用COV估算观察到最接近、偏差最小且因此最准确的估算。在一个实际数据示例中，所有方法都获得了与外部参考相当相似的结果。然而，方法之间的一些差异是有意义的，并且使用COV方法获得了最合理和一致的结果。
基于理论上最准确的估算和实际合理的估计，个体COV方法优于人群平均限定日剂量技术，尽管后者在某些情况下可能是合理的。对于给药异质性特别大的药物治疗，预计COV方法的优势会更大。版权所有© 2016 John Wiley & Sons, Ltd.
