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

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

WEB OF SCIENCE

0

초록

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.

키워드

Bayesian inference; drug interaction; Metropolis-Hastings algorithm; self-controlled case series; tramadol; 메트로폴리스-해스팅스 알고리즘; 베이지안 추론; 약물상호작용; 자기-대조환자군 연구
제목
베이지안 포아송 모형을 적용한 자기-대조 환자군 연구에서의 약물상호작용 위험도 분석
제목 (타언어)
A Bayesian Poisson model for analyzing adverse drug reaction in self-controlled case series studies
저자
이은채; 황범석
DOI
10.5351/KJAS.2020.33.2.203
발행일
2020-04
유형
Article
저널명
응용통계연구
권
33
호
2
페이지
203 ~ 213