title: Zero-inflated Poisson model based likelihood ratio test for drug safety signal detection.
abstract: In recent decades, numerous methods have been developed for data mining of large drug safety databases, such as Food and Drug Administration's (FDA's) Adverse Event Reporting System, where data matrices are formed by drugs such as columns and adverse events as rows. Often, a large number of cells in these data matrices have zero cell counts and some of them are "true zeros" indicating that the drug-adverse event pairs cannot occur, and these zero counts are distinguished from the other zero counts that are modeled zero counts and simply indicate that the drug-adverse event pairs have not occurred yet or have not been reported yet. In this paper, a zero-inflated Poisson model based likelihood ratio test method is proposed to identify drug-adverse event pairs that have disproportionately high reporting rates, which are also called signals. The maximum likelihood estimates of the model parameters of zero-inflated Poisson model based likelihood ratio test are obtained using the expectation and maximization algorithm. The zero-inflated Poisson model based likelihood ratio test is also modified to handle the stratified analyses for binary and categorical covariates (e.g. gender and age) in the data. The proposed zero-inflated Poisson model based likelihood ratio test method is shown to asymptotically control the type I error and false discovery rate, and its finite sample performance for signal detection is evaluated through a simulation study. The simulation results show that the zero-inflated Poisson model based likelihood ratio test method performs similar to Poisson model based likelihood ratio test method when the estimated percentage of true zeros in the database is small. Both the zero-inflated Poisson model based likelihood ratio test and likelihood ratio test methods are applied to six selected drugs, from the 2006 to 2011 Adverse Event Reporting System database, with varying percentages of observed zero-count cells.
authors.affiliation: 1 Division of Biometrics V, Office of Biostatistics, OTS, CDER, FDA, Silver Spring, USA.
authors.forename: Lan
authors.initials: L
authors.lastname: Huang
authors.affiliation: 2 Department of Statistics, University of Missouri, Columbia, USA.
authors.forename: Dan
authors.initials: D
authors.lastname: Zheng
authors.affiliation: 3 Division of Biometrics V, Office of Biostatistics, OTS, CDER, FDA, Silver Spring, USA.
authors.forename: Jyoti
authors.initials: J
authors.lastname: Zalkikar
authors.affiliation: 4 Immediate Office, Office of Biostatistics, OTS, CDER, FDA, Silver Spring, USA.
authors.forename: Ram
authors.initials: R
authors.lastname: Tiwari
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D001241:Aspirin / Q000009:adverse effects; D057225:Data Mining; D016208:Databases, Factual; D005233:Fatty Alcohols / Q000009:adverse effects; D005260:Female; D019786:Gadolinium DTPA / Q000009:adverse effects; D006493:Heparin / Q000009:adverse effects; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008536:Meglumine / Q000009:adverse effects / Q000031:analogs & derivatives; D009942:Organometallic Compounds / Q000009:adverse effects; D016012:Poisson Distribution; D011241:Prednisone / Q000009:adverse effects; D012107:Research Design; D014481:United States; D014486:United States Food and Drug Administration / Q000331:legislation & jurisprudence
chemical_list: D005233:Fatty Alcohols; D009942:Organometallic Compounds; C064572:gadobenic acid; D008536:Meglumine; D006493:Heparin; C529236:docosanol; D019786:Gadolinium DTPA; D001241:Aspirin; D011241:Prednisone
keywords: adverse event reporting system database; expectation and maximization algorithm; false discovery rate; likelihood ratio test; type I error