[Evaluation of estimation of prevalence ratio using bayesian log-binomial regression model].
To evaluate the estimation of prevalence ratio (PR) by using bayesian log-binomial regression model and its application, we estimated thePRof medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software. The results showed that caregivers' recognition of infant' s risk signs of diarrhea was associated significantly with a 13% increase of medical care-seeking. Meanwhile, we compared the differences inPR's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1: not adjusting for the covariates; model 2: adjusting for duration of caregivers' education, model 3: adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model. The results showed that all three bayesian log-binomial regression models were convergence and the estimatedPRswere 1.130(95%CI: 1.005-1.265), 1.128(95%CI: 1.001-1.264) and 1.132(95%CI: 1.004-1.267), respectively. Conventional log-binomial regression model 1 and model 2 were convergence and theirPRswere 1.130(95% CI: 1.055-1.206) and 1.126(95% CI: 1.051-1.203), respectively, but the model 3 was misconvergence, so COPY method was used to estimatePR, which was 1.125 (95%CI: 1.051-1.200). In addition, the point estimation and interval estimation ofPRsfrom three bayesian log-binomial regression models differed slightly from those ofPRsfrom conventional log-binomial regression model, but they had a good consistency in estimatingPR. Therefore, bayesian log-binomial regression model can effectively estimatePRwith less misconvergence and have more advantages in application compared with conventional log-binomial regression model.
Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou 730000, China.
W L
WL
Gao
Department of Computer Software, School of Information and Engineering, Lanzhou University, Lanzhou, 730000, China.
H
H
Lin
Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou 730000, China.
X N
XN
Liu
Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou 730000, China.
X W
XW
Ren
Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou 730000, China.
J S
JS
Li
Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou 730000, China.
X P
XP
Shen
Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou 730000, China.
S L
SL
Zhu
D001499:Bayes Theorem; D017028:Caregivers; D003967:Diarrhea / Q000628:therapy; D015438:Health Behavior; D007722:Health Knowledge, Attitudes, Practice; D006801:Humans; D015233:Models, Statistical; D015995:Prevalence; D012306:Risk
Bayes theorem; Log-binomial regression; Models, statistical; Prevalence ratio; Regression analysis