稳健Poisson和log-binomial的GEE模型应用于非独立数据的研究  

A simulation/case study under the use of robust Poisson and log-binomial model with generalized estimating equation models regarding non-independent data

作　　者：周舒冬[1] 郜艳晖[1] 李丽霞[1] 张敏[1] 杨翌[1] 陈跃[2] 

机构地区：[1]广东药学院公共卫生学院流行病与卫生统计学系广东省分子流行病学重点实验室,广州510310 [2]加拿大渥太华大学流行病与社区医学系

出　　处：《中华流行病学杂志》2014年第4期449-452,共4页Chinese Journal of Epidemiology

基　　金：广东省自然科学基金（10151022401000018）

摘　　要：探讨流行病学资料中非独立数据的RR/患病率比（PR）的合适估计方法.采用计算机模拟实验和实例分析观察稳健Poisson-GEE和log-binomial-GEE模型的适用性并进行比较.结果表明log-binomial-GEE模型与稳健Poisson-GEE模型的收敛率基本均为100％,两模型估计各参数的平均值均与真值接近;在类内聚集性变小或类别数增加时,两模型估计各参数的95％CI覆盖率均有所提高;稳健Poisson-GEE模型对参数估计的稳健性较好,应用到实例时可正确评价暴露对结局的影响.稳健Poisson和log-binomial的GEE模型很少存在收敛问题,且有较高的准确率,可用于流行病学资料中非独立数据的RR/PR值估计.To explore the appropriate method in estimating relative risk （RR）/prevalence ratio （PR） related to non-independent datasets.The simulation datasets generated by computer and case study were analyzed by two generalized estimating equation （GEE） models to investigate and compare the related applicability.Both convergence effects of log-binomial-GEE model and Robust Poisson-GEE model were almost 100%.The estimation results of the two GEE models were both closer to the true value.95%CI coverage of the two GEE models increased along with the reduction of class aggregation or the increase of the number of categories.Robust-Poisson-GEE model seemed to be more stable and steady than the log-binomial-GEE.The two GEE models could correctly evaluate the effects of exposure on the outcome in the case study.Rarely,there appeared problems on convergence of Robust Poisson or log-binomial-GEE model,and the accuracy was high.Both models could be used to estimate the RR/PR on non-independent epidemiological data.

关 键 词：稳健Poisson回归 log-binomial模型 非独立 广义估计方程 

分 类 号：R181.2[医药卫生—流行病学]