Chen Geng, Luo Sheng
Clinical Statistics, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, PA, 19426, USA.
Department of Biostatistics, The University of Texas Health Science Center at Houston, 1200 Pressler St, Houston, TX, 77030, USA.
Biom J. 2016 Jul;58(4):831-51. doi: 10.1002/bimj.201400255. Epub 2015 Dec 29.
The multilevel item response theory (MLIRT) models have been increasingly used in longitudinal clinical studies that collect multiple outcomes. The MLIRT models account for all the information from multiple longitudinal outcomes of mixed types (e.g., continuous, binary, and ordinal) and can provide valid inference for the overall treatment effects. However, the continuous outcomes and the random effects in the MLIRT models are often assumed to be normally distributed. The normality assumption can sometimes be unrealistic and thus may produce misleading results. The normal/independent (NI) distributions have been increasingly used to handle the outlier and heavy tail problems in order to produce robust inference. In this article, we developed a Bayesian approach that implemented the NI distributions on both continuous outcomes and random effects in the MLIRT models and discussed different strategies of implementing the NI distributions. Extensive simulation studies were conducted to demonstrate the advantage of our proposed models, which provided parameter estimates with smaller bias and more reasonable coverage probabilities. Our proposed models were applied to a motivating Parkinson's disease study, the DATATOP study, to investigate the effect of deprenyl in slowing down the disease progression.
多级项目反应理论（MLIRT）模型在收集多个结果的纵向临床研究中越来越常用。MLIRT模型考虑了来自多种类型（如连续型、二元型和有序型）多个纵向结果的所有信息，并能为总体治疗效果提供有效的推断。然而，MLIRT模型中的连续结果和随机效应通常假定为正态分布。正态性假设有时可能不现实，因此可能产生误导性结果。为了得出稳健的推断，正态/独立（NI）分布越来越多地用于处理异常值和重尾问题。在本文中，我们开发了一种贝叶斯方法，该方法在MLIRT模型的连续结果和随机效应上都采用了NI分布，并讨论了实施NI分布的不同策略。进行了广泛的模拟研究以证明我们提出的模型的优势，该模型提供了偏差较小且覆盖概率更合理的参数估计。我们提出的模型应用于一项具有启发性的帕金森病研究——DATATOP研究，以研究司来吉兰在减缓疾病进展方面的作用。
