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

• 成果类型: Article
• 署名作者: Huang, Lan; Zalkikar, Jyoti; Tiwari, Ram C.
• 署名单位: US Food & Drug Administration (FDA); US Food & Drug Administration (FDA)
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2011.ap10243
• 发表日期: 2011
• 页码: 1230-1241
• 关键词: data-mining algorithms; reporting system; pharmacovigilance; generation; events

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.