Dr. Gary R. Cutter, professor in the department of biostatistics at the University of Alabama at Birmingham, and an international team of investigators recently analyzed the results produced by two randomized phase 3 clinical trials examining laquinimod usage in patients with relapsing-remitting multiple sclerosis (MS), employing a propensity score model to assess if differences in the results could be explained by underlying covariates. Their analysis was made up of two main stages: 1) computation of a propensity score (the probability of being assigned to the laquinimod versus the placebo study group) for each trial participant, taking into account a wide range of covariates at baseline including second-degree interactions; and 2) factoring in the propensity score as an additional covariate to the previously defined primary analysis model to assess the effect on the annualized relapse rate (ARR) of laquinimod (0.6 mg/d) as opposed to the placebo.
[Photo: Dr. Gary R. Cutter]
Dr. Cutter observed that “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 versus 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.”
He and his colleagues concluded that propensity scores taking into account differences in baseline characteristics may aid in a better understanding of whether treatment effect differences noted in randomized controlled trials prove to be accurate or are caused by inherent differences among MS patients.
“Laquinimod Efficacy in Relapsing-Remitting Multiple Sclerosis: How to Understand Why and If Studies Disagree” was published in September 2016 in the journal BMC Neurology.