摘要
目的探讨临床试验重复测量资料的统计分析方法。方法通过实例说明并比较各种固定效应模型和混合模型的优缺点。结果临床试验研究资料常为重复测量资料,比较各处理组的测量值差别是否有显著性,可以采用传统的统计方法如t检验、方差分析和协方差分析等;也可以采用混合模型对整个研究过程中所有时点的测量值进行分析。结论由于在重复测量资料中,同一受试者的不同观测值之间具有相关性特点,故对其指定协方差结构尤其重要。Mixed过程提供了丰富的协方差结构,可以充分利用重复测量资料的信息,又能处理缺失值,是重复测量资料最优的统计分析方法。
Objective To explore the statistical methods for repeated measures data. Methods Using an example to illustrate different statistical models（fixed effects models and mixed models）in SAS procedures for repeated measures data in clinical trials. Results There are two important data analysis strategies for comparing the different effects between the two treatment groups： （1）Fixed effects model, such as independent t test, analysis of variance and analysis of covariance; （2）Linear Mixed Models. Conclusion Modeling the covariance structure is especially important for analysis of repeated measures data because measurements taken close in time are potentially more highly correlated than those taken far apart in time. And the MIXED procedure of the SAS System provides a rich selection of covariance structures through the RANDOM and REPEATED statements.
出处
《中国卫生统计》
CSCD
北大核心
2007年第2期140-142,共3页
Chinese Journal of Health Statistics
关键词
临床试验
重复测量
混合模型
Clinical trials
Repeated measures data
Mixed model
