Novakovic A M, Krekels E H J, Munafo A, Ueckert S, Karlsson M O
Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden.
Division of Pharmacology, Leiden Academic Centre of Drug Research, Leiden University, Leiden, Netherlands.
AAPS J. 2017 Jan;19(1):172-179. doi: 10.1208/s12248-016-9977-z. Epub 2016 Sep 15.
In this study, we report the development of the first item response theory (IRT) model within a pharmacometrics framework to characterize the disease progression in multiple sclerosis (MS), as measured by Expanded Disability Status Score (EDSS). Data were collected quarterly from a 96-week phase III clinical study by a blinder rater, involving 104,206 item-level observations from 1319 patients with relapsing-remitting MS (RRMS), treated with placebo or cladribine. Observed scores for each EDSS item were modeled describing the probability of a given score as a function of patients' (unobserved) disability using a logistic model. Longitudinal data from placebo arms were used to describe the disease progression over time, and the model was then extended to cladribine arms to characterize the drug effect. Sensitivity with respect to patient disability was calculated as Fisher information for each EDSS item, which were ranked according to the amount of information they contained. The IRT model was able to describe baseline and longitudinal EDSS data on item and total level. The final model suggested that cladribine treatment significantly slows disease-progression rate, with a 20% decrease in disease-progression rate compared to placebo, irrespective of exposure, and effects an additional exposure-dependent reduction in disability progression. Four out of eight items contained 80% of information for the given range of disabilities. This study has illustrated that IRT modeling is specifically suitable for accurate quantification of disease status and description and prediction of disease progression in phase 3 studies on RRMS, by integrating EDSS item-level data in a meaningful manner.
在本研究中，我们报告了首个在药代动力学框架内的项目反应理论（IRT）模型的开发，该模型用于表征多发性硬化症（MS）的疾病进展，通过扩展残疾状态评分（EDSS）来衡量。数据由一名盲态评分者从一项为期96周的III期临床研究中每季度收集一次，涉及1319例复发缓解型MS（RRMS）患者的104,206个项目级观察值，这些患者接受了安慰剂或克拉屈滨治疗。使用逻辑模型对每个EDSS项目的观察分数进行建模，将给定分数的概率描述为患者（未观察到的）残疾程度的函数。来自安慰剂组的纵向数据用于描述疾病随时间的进展，然后将模型扩展到克拉屈滨组以表征药物效果。计算每个EDSS项目相对于患者残疾程度的敏感性作为费舍尔信息，并根据它们所包含的信息量进行排名。IRT模型能够在项目和总体水平上描述基线和纵向EDSS数据。最终模型表明，克拉屈滨治疗显著减缓了疾病进展速度，与安慰剂相比，疾病进展速度降低了20%，与暴露无关，并且对残疾进展有额外的暴露依赖性降低。八个项目中的四个在给定的残疾范围内包含了80%的信息。这项研究表明，IRT建模通过以有意义的方式整合EDSS项目级数据，特别适用于RRMS的3期研究中疾病状态的准确量化以及疾病进展的描述和预测。
