Data Mining for Adverse Drug Events With a Propensity Score-matched Tree-based Scan Statistic.
Data Mining for Adverse Drug Events With a Propensity Score-matched Tree-based Scan Statistic.
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DOI：
10.1097/ede.0000000000000907
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发表时间：
2018-11
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影响因子：
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通讯作者：
Kulldorff M
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作者：
Wang SV;Maro JC;Baro E;Izem R;Dashevsky I;Rogers JR;Nguyen M;Gagne JJ;Patorno E;Huybrechts KF;Major JM;Zhou E;Reidy M;Cosgrove A;Schneeweiss S;Kulldorff M
基于树的扫描统计是一种统计数据挖掘工具，已被用于疫苗安全性研究中的自控设计信号检测。该重复性统计量调整了对数千起潜在不良事件进行评价的多次测试。然而，许多药物安全性问题并不适合自我对照分析。我们提出了一种方法，结合基于树的扫描统计与倾向得分匹配分析的新的启动器队列，一个强大的设计调查的药物安全性。我们进行了等离子体模拟，以评估性能。在多个现实场景中，倾向评分匹配以调整混杂因素的队列中基于树的扫描统计优于不匹配队列中基于树的扫描统计。在混杂因素使点估计值远离空值的情况下，调整后的分析恢复了预先指定的1型错误，而未调整的分析则增加了1型错误。在混杂因素使点估计值向空值移动的情况下，调整后的分析保留了功效，而未调整的分析大大降低了功效。虽然完全校正真实混杂因素具有最佳性能，但与未校正相比，中度错误指定倾向评分的匹配显著改善了1型错误和功效。当不良事件的风险确实升高时，通常会出现临床相关概念的共同发生信号。TreeScan与倾向评分匹配显示出作为潜在不良事件筛选和优先排序方法的前景。随后应进行专门设计的临床评价和安全性研究，以量化效应的大小，并针对感兴趣的结局进行混杂控制。
The tree-based scan statistic is a statistical data mining tool that has been used for signal detection with a self-controlled design in vaccine safety studies. This disproportionality statistic adjusts for multiple testing in evaluation of thousands of potential adverse events. However, many drug safety questions are not well suited for self-controlled analysis. We propose a method that combines tree-based scan statistics with propensity score matched analysis of new initiator cohorts, a robust design for investigations of drug safety. We conducted plasmode simulations to evaluate performance. In multiple realistic scenarios, tree-based scan statistics in cohorts that were propensity score matched to adjust for confounding outperformed tree-based scan statistics in unmatched cohorts. In scenarios where confounding moved point estimates away from the null, adjusted analyses recovered the pre-specified type 1 error while unadjusted analyses inflated type 1 error. In scenarios where confounding moved point estimates toward the null, adjusted analyses preserved power whereas unadjusted analyses greatly reduced power. Although complete adjustment of true confounders had the best performance, matching on a moderately mis-specified propensity score substantially improved type 1 error and power compared to no adjustment. When there was true elevation in risk of an adverse event, there were often co-occurring signals for clinically related concepts. TreeScan with propensity score matching shows promise as a method for screening and prioritization of potential adverse events. It should be followed by clinical review and safety studies specifically designed to quantify the magnitude of effect, with confounding control targeted to the outcome of interest.