Stiger Thomas R, Banerjee Anindita
a Statistics, Pfizer, Inc. , Groton , Connecticut , USA.
J Biopharm Stat. 2013;23(6):1372-82. doi: 10.1080/10543406.2013.834910.
The analysis of schizophrenia studies is plagued by inefficiency and bias due to much missing data. Mixed-effect models for repeated measures designs help address these problems, but to gain even more efficiency it is desirable to judiciously use additional longitudinal data in such designs by comparing treatment groups over multiple time points. Simulations were conducted to compare a profile analysis approach to other commonly used analysis methods in the presence of data missing at random. One gains efficiency by using a composite contrast over multiple time points when the treatment effect over the time points is not substantially different.
由于存在大量缺失数据,精神分裂症研究的分析受到低效率和偏差的困扰。重复测量设计的混合效应模型有助于解决这些问题,但为了提高效率,在这种设计中明智地使用额外的纵向数据,通过在多个时间点比较治疗组是很有必要的。进行了模拟,以比较在随机缺失数据情况下的轮廓分析方法与其他常用分析方法。当各时间点的治疗效果没有显著差异时,通过在多个时间点使用复合对比可以提高效率。