药物不良反应检测中逻辑斯谛回归的贝叶斯模型选择
Bayesian model selection in logistic regression for the detection of adverse drug reactions
摘要：
动机：自发性不良事件报告在检测药物不良反应方面具有很高的潜力。然而，由于其规模，探索这些数据库需要统计方法。在这方面，采取了预防措施。然而，通过将数据投影到列联表上，这些方法对共同处方和掩蔽效应的问题变得敏感。最近，逻辑斯谛回归已被用于Lasso型惩罚，以检测药物和不良事件之间的关联。然而，刑罚价值的选择却在很大程度上影响着刑罚结果的同时，也是一个值得商榷的问题。结果：在本文中，我们建议使用逻辑斯谛回归，其稀疏性被视为模型选择的挑战。由于模型空间巨大，梅特罗波利斯-黑斯廷斯算法通过最大化BIC准则来执行模型选择。因此，我们避免了惩罚或阈值的校准。在法国药物警戒数据库的应用中，将所提出的方法与参考数据集上的成熟方法进行了比较，并获得了更好的阳性和阴性对照率。然而，许多信号没有被所提出的方法检测到。因此，我们得出结论，该方法应与现有药物警戒措施并行使用。
英文摘要：
Motivation: Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, exploring such databases requires statistical methods. In this context, disproportionality measures are used. However, by projecting the data onto contingency tables, these methods become sensitive to the problem of co-prescriptions and masking effects. Recently, logistic regressions have been used with a Lasso type penalty to perform the detection of associations between drugs and adverse events. However, the choice of the penalty value is open to criticism while it strongly influences the results. Results: In this paper, we propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion. Thus, we avoid the calibration of penalty or threshold. During our application on the French pharmacovigilance database, the proposed method is compared to well established approaches on a reference data set, and obtains better rates of positive and negative controls. However, many signals are not detected by the proposed method. So, we conclude that this method should be used in parallel to existing measures in pharmacovigilance.
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