Glueck Deborah H, Li Qian, Macleod Alasdair J, Litkowski Elizabeth M, Yang Xi, Bian Jiang, Ritzhaupt Albert D, Sommer Max, Valle Natercia, Shaw Jessica R, Muller Keith E
Department of Pediatrics, University of Colorado Denver, Aurora, Colorado, United States of America.
Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, United States of America.
PLoS One. 2025 Sep 3;20(9):e0329712. doi: 10.1371/journal.pone.0329712. eCollection 2025.
GLIMMPSE Version 3 is a free, web-based, open-source software tool, which calculates power and sample size for general linear mixed models with Gaussian errors. The software permits power calculations for clinical trials, randomized experiments, and observational studies with clustering, repeated measures, and both, and almost any testable hypothesis. The software has been supported by five United States National Institutes of Health (NIH) grants, is used for more than 14,000 power or sample size calculations per year, has been cited in almost 500 peer-reviewed manuscripts, and used to design more than 200 million dollars in NIH-funded studies. This release provides several new features. The back end has been refactored in Python. The interface has been simplified, requiring user decisions about only one topic per screen. A new menu improves specification of both between-participant and within-participant hypotheses. A recursive algorithm permits computing covariances for up to ten levels of clustering. An updated Monte Carlo simulation using five new examples with clustering, longitudinality, or both, shows accuracy of the power approximations to within 0.01. Five new examples demonstrate power or sample size calculations for 1) a cluster-randomized trial, 2) a longitudinal study with repeated measures, 3) a multilevel study with a multivariate outcome, 4) a multilevel and longitudinal study, and 5) a complex study with a subgroup factor, repeated measures, and intervention-by-location interaction.
GLIMMPSE版本3是一款免费的基于网络的开源软件工具,用于计算具有高斯误差的一般线性混合模型的功效和样本量。该软件可用于临床试验、随机实验以及存在聚类、重复测量或两者皆有的观察性研究的功效计算,以及几乎任何可检验的假设。该软件获得了美国国立卫生研究院(NIH)的五项资助,每年用于超过14000次功效或样本量计算,被近500篇同行评审手稿引用,并用于设计由NIH资助的超过2亿美元的研究。此版本提供了几个新功能。后端已用Python进行了重构。界面得到了简化,每个屏幕仅需用户就一个主题做出决策。一个新菜单改进了参与者间和参与者内假设的规范。一种递归算法允许计算多达十个聚类层次的协方差。使用五个包含聚类、纵向性或两者皆有的新示例进行的更新蒙特卡罗模拟显示,功效近似值的准确度在0.01以内。五个新示例展示了以下情况的功效或样本量计算:1)一项整群随机试验;2)一项具有重复测量的纵向研究;3)一项具有多变量结果的多层次研究;4)一项多层次和纵向研究;5)一项具有亚组因素、重复测量以及干预与地点交互作用的复杂研究。