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Background and aims: Various attempts have been made to support recovery following optic neuritis (ON), but the respective trials have mostly been negative. The aim of this study was to determine whether disease-modifying treatment (DMT) following ON as first manifestation of relapsing-remitting multiple sclerosis influences long-term outcomes. Methods: A total of 79 patients with ON were identified and evaluated at relapse, DMT induction, and 12 months following treatment induction with either glatiramer acetate (GLAT), interferon-beta (IFN), or teriflunomide (TRF). Low-contrast letter acuity (LCLA) and full-field visual-evoked potentials (FF-VEP) were compared between treatment groups using multivariable regression models. The impact of TRF treatment induction compared with IFN or GLAT following relapses outside the optic nerves was evaluated in an independent cohort of 122 patients. Magnetic resonance imaging (MRI) outcomes and rates of confirmed improvement of relapse-related disability were evaluated. Results: TRF-treated patients exhibited higher LCLA and lower relative P100 latencies normalized to the fellow-eye. Findings were significant following covariate-adjustment by multivariable analyses. Cranial MRI lesion load as well as disability progression rates were not significantly different between groups. The cohort of patients following relapses other than ON showed no differences in confirmed improvement of disability. Conclusion: TRF treatment is associated with favorable outcomes regarding functional optic nerve recovery following ON in early multiple sclerosis. [ABSTRACT FROM AUTHOR]

The one-sample log-rank test allows to compare the survival of a single sample with a prefixed reference survival curve. It naturally applies in single-arm phase IIa trials with time-to-event endpoint. Several authors have described that the original one-sample log-rank test is conservative when sample size is small and have proposed strategies to correct the conservativeness. Here, we propose an alternative approach to improve the one-sample log-rank test. Our new one-sample log-rank statistic is based on the unique transformation of the underlying counting process martingale such that the moments of the limiting normal distribution have no shared parameters. Simulation results show that the new one-sample log-rank test gives type I error rate and power close to the nominal levels also when sample size is small, while relevantly reducing the required sample size to achieve the desired power as compared to current approaches to design studies to compare the survival outcome of a sample with a reference. [ABSTRACT FROM AUTHOR]