Zero-inflated Poisson model based likelihood ratio test for drug safety signal detection

2016 ◽  
Vol 26 (1) ◽  
pp. 471-488 ◽  
Author(s):  
Lan Huang ◽  
Dan Zheng ◽  
Jyoti Zalkikar ◽  
Ram Tiwari
Keyword(s):  
Signal Detection ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Drug Safety ◽  
Poisson Model ◽  
Ratio Test ◽  
Safety Signal ◽  
Model Based

scholarly journals Likelihood-Ratio-Test Methods for Drug Safety Signal Detection from Multiple Clinical Datasets

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Lan Huang ◽  
Jyoti Zalkikar ◽  
Ram Tiwari
Keyword(s):  
Signal Detection ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Drug Safety ◽  
Drug Exposure ◽  
Type I Error ◽  
Test Methods ◽  
Ratio Test ◽  
Type I ◽  
Safety Signal

Pre- and postmarket drug safety evaluations usually include an integrated summary of results obtained using data from multiple studies related to a drug of interest. This paper proposes three approaches based on the likelihood ratio test (LRT), called the LRT methods, for drug safety signal detection from large observational databases with multiple studies, with focus on identifying signals of adverse events (AEs) from many AEs associated with a particular drug or inversely for signals of drugs associated with a particular AE. The methods discussed include simple pooled LRT method and its variations such as the weighted LRT that incorporates the total drug exposure information by study. The power and type-I error of the LRT methods are evaluated in a simulation study with varying heterogeneity across studies. For illustration purpose, these methods are applied to Proton Pump Inhibitors (PPIs) data with 6 studies for the effect of concomitant use of PPIs in treating patients with osteoporosis and to Lipiodol (a contrast agent) data with 13 studies for evaluating that drug’s safety profiles.

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