A Likelihood Ratio Test Based Method for Signal Detection With Application to FDA's Drug Safety Data

被引:83
作者
Huang, Lan [1 ]
Zalkikar, Jyoti
Tiwari, Ram C. [2 ]
机构
[1] US FDA, DBV, OB, CDER, Silver Spring, MD 20993 USA
[2] US FDA, Off Biostat, CDER, Silver Spring, MD 20993 USA
关键词
AERS database; Disproportionality signal detection; False discovery rate; Reporting rate; Simulation; DATA-MINING ALGORITHMS; REPORTING SYSTEM; PHARMACOVIGILANCE; GENERATION; EVENTS;
D O I
10.1198/jasa.2011.ap10243
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Several statistical methods that are available in the literature to analyze postmarket safety databases, such as the U.S. Federal Drug Administration's (FDA) adverse event reporting system (AERS), for identifying drug-event combinations with disproportionately high frequencies, are subject to high false discovery rates. Here, we propose a likelihood ratio test (LRT) based method and show, via an extensive simulation study, that the proposed method while retaining good power and sensitivity for identifying signals, controls both the Type I error and false discovery rates. The application of the LRT method to the AERS database is illustrated using two datasets; a small dataset consisting of suicidal behavior and mood change-related AE cases for the drug Montelukast, and a large dataset consisting of all possible AE cases reported to FDA during 2004-2008 for the drug Heparin. This article has supplementary material online.
引用
收藏
页码:1230 / 1241
页数:12
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