The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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A Bayesian Poisson model for analyzing adverse drug reaction in self-controlled case series studies

베이지안 포아송 모형을 적용한 자기-대조 환자군 연구에서의 약물상호작용 위험도 분석

  • Lee, Eunchae (Department of Applied Statistics, Chung-Ang University) ;
  • Hwang, Beom Seuk (Department of Applied Statistics, Chung-Ang University)
  • 이은채 (중앙대학교 응용통계학과) ;
  • 황범석 (중앙대학교 응용통계학과)
  • Received : 2020.01.07
  • Accepted : 2020.01.11
  • Published : 2020.04.30
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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.

자기-대조 환자군(self-controlled case series; SCCS) 연구는 별도의 대조군 없이 환자의 비노출기간을 대조기간으로 설정하여 노출기간에 대한 상대적인 발생 위험도를 측정하는 역학 연구의 한 방법이다. 이 방법은 대조군을 선정할 때 발생하는 편의를 최소화할 수 있는 장점이 있어서 약물 복용 후 이상반응 발생 위험도를 측정하기 위한 방법으로 전통적으로 많이 사용되어왔다. 본 연구는 SCCS 연구를 바탕으로 두 개 이상의 약물을 동시에 사용했을 때 그 부작용의 위험이 어떻게 증가하는지 살펴보고자 한다. 마약성 진통제 유사체인 tramadol과 다빈도 병용 약물 간 약물상호작용에 대해 조건부 포아송 모형을 가정하고 분석하였다. 이때 베이지안 추론법을 사용하여 최대가능도추정량이 지니고 있는 과대적합 문제를 해결하며, 사전분포의 민감도를 측정하기 위해 정규 사전분포와 라플라스 사전분포를 가정하여 모형화하였다.

Keywords

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