[Evaluation of estimation of prevalence ratio using bayesian log-binomial regression model].

To evaluate the estimation of prevalence ratio () by using bayesian log-binomial regression model and its application, we estimated the of 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 in '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 estimated were 1.130(95: 1.005-1.265), 1.128(95: 1.001-1.264) and 1.132(95: 1.004-1.267), respectively. Conventional log-binomial regression model 1 and model 2 were convergence and their were 1.130(95: 1.055-1.206) and 1.126(95: 1.051-1.203), respectively, but the model 3 was misconvergence, so COPY method was used to estimate , which was 1.125 (95: 1.051-1.200). In addition, the point estimation and interval estimation of from three bayesian log-binomial regression models differed slightly from those of from conventional log-binomial regression model, but they had a good consistency in estimating . Therefore, bayesian log-binomial regression model can effectively estimate with less misconvergence and have more advantages in application compared with conventional log-binomial regression model.