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Activity Number:
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231
- SPEED: SPAAC SESSION I
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Type:
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Topic-Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Biometrics Section
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Abstract #317695
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Title:
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Semiparametric Panel Count Model, with Applications to Signal Detection in Post-Market Drug Surveillance Systems
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Author(s):
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Yizhao Zhou* and Ao Yuan and Ming Tan
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Companies:
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Georgetown University and Georgetown University and Georgetown University
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Keywords:
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Covariate information;
Drug safety;
Signal detection;
Semi-parametric panel count model
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Abstract:
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Panel count data occurs in a wide variety of applications ranging from biomedical research to business. Notably, millions of reported adverse events (AEs) associated with thousands of drugs are monitored in the post-market drug safety surveillance systems worldwide. Evaluating the AEs of the associated drugs is of important public health concern and motivates our method. One statistical challenge in such systems is to handle the excessive zero AE counts. Most existing methods utilize Poisson counts models that cannot incorporate covariates nor account for the excessive zero counts adequately. This article proposes a novel semi-parametric panel count model to detect AE signals by accounting for covariates, background AE occurrences, and excessive zero counts. We develop an estimating procedure with Expectation-Maximization (EM) algorithm to estimate the model. The strong consistency and the asymptotic normality of the estimators are formally derived. We conduct simulation studies to evaluate the finite sample performance of the method proposed and to demonstrate the apparent advantage of the proposed method in signal detection. We apply the method to a VigiBase dataset.
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Authors who are presenting talks have a * after their name.
