과대산포가 존재하는 이항형 자료의 회귀분석방법

• 영문명: Regression Methods for Overdispersed Dichotomous Response Data
• 발행기관: 대한신경정신의학회
• 저자명: 김동기 한무영 전우택 명성민 송기준
• 간행물 정보: 『신경정신의학』제44권 제5호, 549~552쪽, 전체 4쪽
• 주제분류: 의약학 > 정신과학
• 파일형태: PDF
• 발행일자: 2005.09.30

영문 초록

In neuropsychiatrical research, many problems of statistical inference concern the relationship between the PTSD and traumatic experiences. The logistic model is widely used for modeling a relationship between the covariate and the magnitude of the PTSD. A common complication in the logistic model for dichotomous response data is overdispersion. In this study, two different methods for analyzing dichotomous response data are illustrated and compared. One method is the logistic regression approach, where the numbers of dichotomous responses are predicted by the logistic function of covariates. The other one is the overdispersed logistic regression approach, where the overdispersion is measured by a scale parameter in the variance function of the dichotomous response. In dichotomous response model, when reponses are overdispersed, the overdispersed logistic regression produces more appropriate standard errors of the regression coefficients and the 95% confidence intervals of odds ratios. Therefore, in neuropsychiatrical research, it is recommended to examine the overdispersion problems for their data set before applying the logistic regression model

목차

서 론

로지스틱 회귀모형과 과대산포 로지스틱 회귀분석

두 방법의 결과 비교

결 론

REFERENCES

키워드

Logistic regression Overdispersion PTSD Dichotomous response data