Laquinimod efficacy in relapsing-remitting multiple sclerosis: how to understand why and if studies disagree

拉喹莫德治疗复发缓解型多发性硬化症的疗效:如何理解研究结果不一致的原因及原因

阅读:1

Abstract

BACKGROUND: The results of two randomized phase 3 trials that investigated the use of laquinimod in patients with relapsing-remitting multiple sclerosis were analyzed using a propensity score model. METHODS: The propensity score in each study was defined as the probability of an individual patient being assigned to either the laquinimod or placebo study arm. The analysis included two main stages: (1) calculation of a propensity score for each patient, given a broad set of baseline covariates that included second-degree interactions, and (2) incorporation of the propensity score as another covariate into the predefined primary analysis model to test the treatment effect of laquinimod (0.6 mg/d) vs placebo on the annualized relapse rate (ARR). RESULTS: The BRAVO study showed baseline imbalances for T2 volume and the proportion of patients with gadolinium (Gd)-enhancing lesions, both parameters known to correlate with risk of relapse. Adjustment using the propensity score as a categorical variable showed that the estimated difference in ARR between laquinimod and placebo was 0.078, in favor of laquinimod. In ALLEGRO, the baseline Gd-enhancing lesion mean score was higher for placebo vs laquinimod. When the primary analysis model was adjusted for the propensity score as a categorical variable, the covariate adjusted difference in mean ARR between laquinimod and placebo was 0.084, in favor of laquinimod. CONCLUSIONS: Propensity scores addressing differences in baseline characteristics may be helpful to better understand whether observed treatment effect differences in randomized controlled trials are accurate results or result from inherent differences between patients with multiple sclerosis.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。