Lu Tong, Yang Yujie, Jin Jin Y, Kågedal Matts
Department of Clinical Pharmacology, Genentech, Inc, South San Francisco, California, USA.
Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA.
CPT Pharmacometrics Syst Pharmacol. 2020 Feb;9(2):96-105. doi: 10.1002/psp4.12487. Epub 2020 Jan 30.
Longitudinal-ordered categorical data, common in clinical trials, can be effectively analyzed with nonlinear mixed effect models. In this article, we systematically evaluated the performance of three different models in longitudinal muscle spasm adverse event (AE) data obtained from a clinical trial for vismodegib: a proportional odds (PO) model, a discrete-time Markov model, and a continuous-time Markov model. All models developed based on weekly spaced data can reasonably capture the proportion of AE grade over time; however, the PO model overpredicted the transition frequency between grades and the cumulative probability of AEs. The influence of data frequency (daily, weekly, or unevenly spaced) was also investigated. The PO model performance reduced with increased data frequency, and the discrete-time Markov model failed to describe unevenly spaced data, but the continuous-time Markov model performed consistently well. Clinical trial simulations were conducted to illustrate the muscle spasm resolution time profile during the 8-week dose interruption period after 12 weeks of continuous treatment.
纵向有序分类数据在临床试验中很常见，可以用非线性混合效应模型进行有效分析。在本文中，我们系统评估了三种不同模型在从维莫德吉的一项临床试验中获得的纵向肌肉痉挛不良事件（AE）数据中的表现：比例优势（PO）模型、离散时间马尔可夫模型和连续时间马尔可夫模型。所有基于每周间隔数据开发的模型都能合理地捕捉随时间变化的AE等级比例；然而，PO模型高估了等级之间的转换频率和AE的累积概率。还研究了数据频率（每日、每周或间隔不均匀）的影响。PO模型的性能随着数据频率的增加而降低，离散时间马尔可夫模型无法描述间隔不均匀的数据，但连续时间马尔可夫模型始终表现良好。进行了临床试验模拟，以说明在连续治疗12周后的8周剂量中断期内肌肉痉挛缓解时间的情况。
