BACKGROUND AND OBJECTIVE: We investigated a method to evaluate a treatment switching approach, namely treatment change after one multiple sclerosis (MS) relapse. METHODS:Patients who experienced a relapse while on a first-line disease-modifying therapy, glatiramer acetate, were identified. Based on their subsequent course, patients were divided into two groups: those who changed treatment and those who did not. Patients were allowed to change to any other treatment. Subsequent annualized relapse rate and time to next relapse were compared in the two groups. Since patients were not randomized to treatment group, negative binomial and Cox regression models were used to control for several potential clinical confounders, including relapse severity, relapse duration, age, disease duration and presence of previous/combination therapy. In addition, an inverse probability of treatment weighting model was used to control for confounding. Several secondary analyses investigated patient subgroups. RESULTS: Statistical modeling showed that there was no significant difference between groups in terms of relapse rate (rate ratio; 95% CI = 0.68; 0.35, 1.31) and time to next relapse (hazard ratio; 95% CI = 0.61; 0.30, 1.25). All secondary analyses confirmed these results. In addition, no significant difference in time to sustained progression on the Expanded Disability Status Scale was observed (p > 0.05). Our approach allowed investigation of the choice to change treatment after a relapse. CONCLUSIONS: Our results showed that a single relapse may not be sufficient to indicate treatment failure. Although clinical confounders were addressed in our modeling, unmeasured confounders, particularly the presence of magnetic resonance imaging activity, may have biased our conclusion.
RCT Entities:
BACKGROUND AND OBJECTIVE: We investigated a method to evaluate a treatment switching approach, namely treatment change after one multiple sclerosis (MS) relapse. METHODS:Patients who experienced a relapse while on a first-line disease-modifying therapy, glatiramer acetate, were identified. Based on their subsequent course, patients were divided into two groups: those who changed treatment and those who did not. Patients were allowed to change to any other treatment. Subsequent annualized relapse rate and time to next relapse were compared in the two groups. Since patients were not randomized to treatment group, negative binomial and Cox regression models were used to control for several potential clinical confounders, including relapse severity, relapse duration, age, disease duration and presence of previous/combination therapy. In addition, an inverse probability of treatment weighting model was used to control for confounding. Several secondary analyses investigated patient subgroups. RESULTS: Statistical modeling showed that there was no significant difference between groups in terms of relapse rate (rate ratio; 95% CI = 0.68; 0.35, 1.31) and time to next relapse (hazard ratio; 95% CI = 0.61; 0.30, 1.25). All secondary analyses confirmed these results. In addition, no significant difference in time to sustained progression on the Expanded Disability Status Scale was observed (p > 0.05). Our approach allowed investigation of the choice to change treatment after a relapse. CONCLUSIONS: Our results showed that a single relapse may not be sufficient to indicate treatment failure. Although clinical confounders were addressed in our modeling, unmeasured confounders, particularly the presence of magnetic resonance imaging activity, may have biased our conclusion.
Authors: Mike P Wattjes; Àlex Rovira; David Miller; Tarek A Yousry; Maria P Sormani; Maria P de Stefano; Mar Tintoré; Cristina Auger; Carmen Tur; Massimo Filippi; Maria A Rocca; Franz Fazekas; Ludwig Kappos; Chris Polman Journal: Nat Rev Neurol Date: 2015-09-15 Impact factor: 42.937
Authors: Tjalf Ziemssen; Ovidiu A Bajenaru; Adriana Carrá; Nina de Klippel; João C de Sá; Astrid Edland; Jette L Frederiksen; Olivier Heinzlef; Klimentini E Karageorgiou; Rafael H Lander Delgado; Anne-Marie Landtblom; Miguel A Macías Islas; Niall Tubridy; Yossi Gilgun-Sherki Journal: J Neurol Date: 2014-08-14 Impact factor: 4.849