Gibbons Robert D, Segawa Eisuke, Karabatsos George, Amatya Anup K, Bhaumik Dulal K, Brown C Hendricks, Kapur Kush, Marcus Sue M, Hur Kwan, Mann J John
Center for Health Statistics, University of Illinois at Chicago, 1601 W. Taylor, Chicago, IL 60612, U.S.A.
Stat Med. 2008 May 20;27(11):1814-33. doi: 10.1002/sim.3241.
A new statistical methodology is developed for the analysis of spontaneous adverse event (AE) reports from post-marketing drug surveillance data. The method involves both empirical Bayes (EB) and fully Bayes estimation of rate multipliers for each drug within a class of drugs, for a particular AE, based on a mixed-effects Poisson regression model. Both parametric and semiparametric models for the random-effect distribution are examined. The method is applied to data from Food and Drug Administration (FDA)'s Adverse Event Reporting System (AERS) on the relationship between antidepressants and suicide. We obtain point estimates and 95 per cent confidence (posterior) intervals for the rate multiplier for each drug (e.g. antidepressants), which can be used to determine whether a particular drug has an increased risk of association with a particular AE (e.g. suicide). Confidence (posterior) intervals that do not include 1.0 provide evidence for either significant protective or harmful associations of the drug and the adverse effect. We also examine EB, parametric Bayes, and semiparametric Bayes estimators of the rate multipliers and associated confidence (posterior) intervals. Results of our analysis of the FDA AERS data revealed that newer antidepressants are associated with lower rates of suicide adverse event reports compared with older antidepressants. We recommend improvements to the existing AERS system, which are likely to improve its public health value as an early warning system.
一种新的统计方法被开发出来，用于分析来自上市后药物监测数据的自发不良事件（AE）报告。该方法基于混合效应泊松回归模型，涉及对一类药物中每种药物针对特定不良事件的发生率乘数进行经验贝叶斯（EB）估计和完全贝叶斯估计。对随机效应分布的参数模型和半参数模型都进行了研究。该方法应用于美国食品药品监督管理局（FDA）不良事件报告系统（AERS）中关于抗抑郁药与自杀之间关系的数据。我们获得了每种药物（如抗抑郁药）发生率乘数的点估计值和95%置信（后验）区间，可用于确定特定药物与特定不良事件（如自杀）的关联风险是否增加。不包括1.0的置信（后验）区间为药物与不良反应之间显著的保护或有害关联提供了证据。我们还研究了发生率乘数的经验贝叶斯、参数贝叶斯和半参数贝叶斯估计量以及相关的置信（后验）区间。我们对FDA AERS数据的分析结果显示，与较老的抗抑郁药相比，较新的抗抑郁药与较低的自杀不良事件报告率相关。我们建议对现有的AERS系统进行改进，这可能会提高其作为早期预警系统的公共卫生价值。
