Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada.
Stat Med. 2012 Feb 28;31(5):449-69. doi: 10.1002/sim.4394. Epub 2011 Oct 3.
Magnetic resonance imaging (MRI) data are routinely collected at multiple time points during phase 2 clinical trials in multiple sclerosis. However, these data are typically summarized into a single response for each patient before analysis. Models based on these summary statistics do not allow the exploration of the trade-off between numbers of patients and numbers of scans per patient or the development of optimal schedules for MRI scanning. To address these limitations, in this paper, we develop a longitudinal model to describe one MRI outcome: the number of lesions observed on an individual MRI scan. We motivate our choice of a mixed hidden Markov model based both on novel graphical diagnostic methods applied to five real data sets and on conceptual considerations. Using this model, we compare the performance of a number of different tests of treatment effect. These include standard parametric and nonparametric tests, as well as tests based on the new model. We conduct an extensive simulation study using data generated from the longitudinal model to investigate the parameters that affect test performance and to assess size and power. We determine that the parameters of the hidden Markov chain do not substantially affect the performance of the tests. Furthermore, we describe conditions under which likelihood ratio tests based on the longitudinal model appreciably outperform the standard tests based on summary statistics. These results establish that the new model is a valuable practical tool for designing and analyzing multiple sclerosis clinical trials.
磁共振成像（MRI）数据在多发性硬化症的 2 期临床试验中通常会在多个时间点进行采集。然而，这些数据通常会在分析前汇总为每个患者的单个反应。基于这些汇总统计数据的模型不允许探索患者数量和每位患者扫描次数之间的权衡，也无法制定最佳的 MRI 扫描计划。为了解决这些限制，在本文中，我们开发了一个纵向模型来描述一个 MRI 结果：个体 MRI 扫描中观察到的病变数量。我们选择基于混合隐马尔可夫模型的理由既基于应用于五个真实数据集的新颖图形诊断方法，也基于概念性考虑。使用该模型，我们比较了许多不同治疗效果检验的性能。这些检验包括标准的参数和非参数检验，以及基于新模型的检验。我们使用从纵向模型生成的数据进行了广泛的模拟研究，以研究影响检验性能的参数，并评估其大小和功效。我们确定隐马尔可夫链的参数不会显著影响检验的性能。此外，我们描述了基于纵向模型的似然比检验明显优于基于汇总统计数据的标准检验的条件。这些结果表明，新模型是设计和分析多发性硬化症临床试验的有价值的实用工具。
