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http://dx.doi.org/10.5351/KJAS.2020.33.2.203

A Bayesian Poisson model for analyzing adverse drug reaction in self-controlled case series studies  

Lee, Eunchae (Department of Applied Statistics, Chung-Ang University)
Hwang, Beom Seuk (Department of Applied Statistics, Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.33, no.2, 2020 , pp. 203-213 More about this Journal
Abstract
The self-controlled case series (SCCS) study measures the relative risk of exposure to exposure period by setting the non-exposure period of the patient as the control period without a separate control group. This method minimizes the bias that occurs when selecting a control group and is often used to measure the risk of adverse events after taking a drug. This study used SCCS to examine the increased risk of side effects when two or more drugs are used in combination. A conditional Poisson model is assumed and analyzed for drug interaction between the narcotic analgesic, tramadol and multi-frequency combination drugs. Bayesian inference is used to solve the overfitting problem of MLE and the normal or Laplace prior distributions are used to measure the sensitivity of the prior distribution.
Keywords
Bayesian inference; drug interaction; Metropolis-Hastings algorithm; self-controlled case series; tramadol;
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