title: Logistic Regression Likelihood Ratio Test Analysis for Detecting Signals of Adverse Events in Post-market Safety Surveillance.
abstract: The Vaccine Adverse Event Reporting System (VAERS) and other product surveillance systems compile reports of product-associated adverse events (AEs), and these reports may include a wide range of information including age, gender, and concomitant vaccines. Controlling for possible confounding variables such as these is an important task when utilizing surveillance systems to monitor post-market product safety. A common method for handling possible confounders is to compare observed product-AE combinations with adjusted baseline frequencies where the adjustments are made by stratifying on observable characteristics. Though approaches such as these have proven to be useful, in this article we propose a more flexible logistic regression approach which allows for covariates of all types rather than relying solely on stratification. Indeed, a main advantage of our approach is that the general regression framework provides flexibility to incorporate additional information such as demographic factors and concomitant vaccines. As part of our covariate-adjusted method, we outline a procedure for signal detection that accounts for multiple comparisons and controls the overall Type 1 error rate. To demonstrate the effectiveness of our approach, we illustrate our method with an example involving febrile convulsion, and we further evaluate its performance in a series of simulation studies.
authors.affiliation: a Merck Research Labs, Merck & Co., Inc, North Wales, Pennsylvania, USA.
authors.forename: Kijoeng
authors.initials: K
authors.lastname: Nam
authors.affiliation: b Department of Oncology, Sidney Kimmel Comprehensive Cancer Center , Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: Nicholas C
authors.initials: NC
authors.lastname: Henderson
authors.affiliation: c Division of Epidemiology, Office of Biostatistics and Epidemiology , CBER , FDA , Silver Spring, Maryland, USA.
authors.forename: Patricia
authors.initials: P
authors.lastname: Rohan
authors.affiliation: c Division of Epidemiology, Office of Biostatistics and Epidemiology , CBER , FDA , Silver Spring, Maryland, USA.
authors.forename: Emily Jane
authors.initials: EJ
authors.lastname: Woo
authors.affiliation: d Division of Biostatistics, Office of Biostatistics and Epidemiology , CBER , FDA , Silver Spring, Maryland, USA.
authors.forename: Estelle
authors.initials: E
authors.lastname: Russek-Cohen
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000592:standards / Q000706:statistics & numerical data*; D006801:Humans; D007252:Influenza Vaccines / Q000009:adverse effects*; D016013:Likelihood Functions; D016015:Logistic Models; D011358:Product Surveillance, Postmarketing / Q000592:standards / Q000706:statistics & numerical data*; D014611:Vaccination / Q000009:adverse effects* / Q000592:standards / Q000706:statistics & numerical data*
chemical_list: D007252:Influenza Vaccines
keywords: Adverse event; VAERS; febrile convulsion; signal detection; vaccine safety