Arnaud Mickael, Salvo Francesco, Ahmed Ismaïl, Robinson Philip, Moore Nicholas, Bégaud Bernard, Tubert-Bitter Pascale, Pariente Antoine
Université de Bordeaux, 146 Rue Léo Saignat, BP 36, 33000, Bordeaux Cedex, France.
INSERM U657, Bordeaux, France.
Drug Saf. 2016 Mar;39(3):251-60. doi: 10.1007/s40264-015-0375-8.
The two methods for minimizing competition bias in signal of disproportionate reporting (SDR) detection--masking factor (MF) and masking ratio (MR)--have focused on the strength of disproportionality for identifying competitors and have been tested using competitors at the drug level.
The aim of this study was to develop a method that relies on identifying competitors by considering the proportion of reports of adverse events (AEs) that mention the drug class at an adequate level of drug grouping to increase sensitivity (Se) for SDR unmasking, and its comparison with MF and MR.
Reports in the French spontaneous reporting database between 2000 and 2005 were selected. Five AEs were considered: myocardial infarction, pancreatitis, aplastic anemia, convulsions, and gastrointestinal bleeding; related reports were retrieved using standardized Medical Dictionary for Regulatory Activities (MedDRA(®)) queries. Potential competitors of AEs were identified using the developed method, i.e. Competition Index (ComIn), as well as MF and MR. All three methods were tested according to Anatomical Therapeutic Chemical (ATC) classification levels 2-5. For each AE, SDR detection was performed, first in the complete database, and second after removing reports mentioning competitors; SDRs only detected after the removal were unmasked. All unmasked SDRs were validated using the Summary of Product Characteristics, and constituted the reference dataset used for computing the performance for SDR unmasking (area under the curve [AUC], Se).
Performance of the ComIn was highest when considering competitors at ATC level 3 (AUC: 62 %; Se: 52 %); similar results were obtained with MF and MR.
The ComIn could greatly minimize the competition bias in SDR detection. Further study using a larger dataset is needed.
在不成比例报告信号（SDR）检测中最小化竞争偏差的两种方法——屏蔽因子（MF）和屏蔽率（MR）——一直专注于识别竞争者时的不成比例强度，并已在药物层面使用竞争者进行了测试。
本研究的目的是开发一种方法，该方法通过在适当的药物分组水平上考虑提及药物类别的不良事件（AE）报告比例来识别竞争者，以提高SDR解蔽的敏感性（Se），并将其与MF和MR进行比较。
选择了2000年至2005年法国自发报告数据库中的报告。考虑了五种AE：心肌梗死、胰腺炎、再生障碍性贫血、惊厥和胃肠道出血；使用标准化的《监管活动医学词典》（MedDRA®）查询检索相关报告。使用开发的方法即竞争指数（ComIn）以及MF和MR识别AE的潜在竞争者。所有三种方法均根据解剖治疗化学（ATC）分类级别2-5进行测试。对于每种AE，首先在完整数据库中进行SDR检测，其次在删除提及竞争者的报告后进行检测；仅在删除后检测到的SDR被解蔽。所有解蔽的SDR均使用产品特征摘要进行验证，并构成用于计算SDR解蔽性能（曲线下面积[AUC]，Se）的参考数据集。
在考虑ATC级别3的竞争者时，ComIn的性能最高（AUC：62%；Se：52%）；MF和MR也获得了类似结果。
ComIn可以极大地最小化SDR检测中的竞争偏差。需要使用更大的数据集进行进一步研究。
