| アブストラクト | 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. |
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| 組織名 | a Merck Research Labs, Merck & Co., Inc, North Wales, Pennsylvania, USA.;b Department of Oncology, Sidney Kimmel Comprehensive Cancer Center , Johns;Hopkins University, Baltimore, Maryland, USA.;c Division of Epidemiology, Office of Biostatistics and Epidemiology , CBER , FDA;, Silver Spring, Maryland, USA.;d Division of Biostatistics, Office of Biostatistics and Epidemiology , CBER ,;FDA , Silver Spring, Maryland, USA. |
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