log-binomial模型估计的患病比及其应用

Using log-binomial model for estimating the prevalence ratio

探讨有和无连续协变量时的log—binomial模型估计患病比的统计方法及其应用。文中使用禁烟立法态度与吸烟关联的研究实例，比较log—binomial模型估计的现患比（PR）与logistic回归模型估计的优势比（OR）。当模型中无连续协变量时，采用最大似然估计拟合log—binomial模型；当因含有连续协变量导致模型不收敛时，则采用COPY方法估计PR。分别估计男、女禁烟立法态度与吸烟的关联。由于女性吸烟率低，用PR与OR所估计的关联结果相似。而男性吸烟率较高，OR值明显大于PR。当年龄作为连续协变量纳入模型时，导致log—binomial模型不收敛，采用COPY方法解决此问题。所有分析均在SAS软件中实现。结论：当患病率较高时，PR比OR更好地测量了暴露与疾病的关联。文中给出10g—binomial回归模型和COPY方法估计P尺的SAS程序。

To estimate the prevalence ratios, using a log-binomial model with or without continuous covariates. Prevalence ratios for individuals＇ attitude towards smoking-ban legislation associated with smoking status, estimated by ratios estimated by logistic regression model using a log-binomial model were compared with odds In the log-binomial modeling, maximum likelihood method was used when there were no continuous covariates and COPY approach was used if the model did not converge, for example due to the existence of continuous covariates. We examined the association between individuals＇ attitude towards smoking-ban legislation and smoking status in men and women. Prevalence ratio and odds ratio estimation provided similar results for the association in women since smoking was not common. In men however, the odds ratio estimates were markedly larger than the prevalence ratios due to a higher prevalence of outcome. The log-binomial model did not converge when age was included as a continuous covariate and COPY method was used to deal with the situation. All analysis was performed by SAS. Prevalence ratio seemed to better measure the association than odds ratio when prevalence is high. SAS programs were provided to calculate the orevalence ratios with or without continuous covariates in the log-binomial regression analysis.