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BackgroundIn multiple sclerosis, impact of treatment on disability progression can be confounded if treatment also reduces relapses.ObjectiveTo distinguish siponimod's direct effects on disability progression from those on relapses in the EXPAND phase 3 trial.MethodsThree estimands, one based on principal stratum and two on hypothetical scenarios (no relapses, or equal relapses in both treatment arms), were defined to determine the extent to which siponimod's effects on 3- and 6-month confirmed disability progression were independent of on-study relapses.ResultsPrincipal stratum analysis estimated that siponimod reduced the risk of 3- and 6-month confirmed disability progression by 14%-20% and 29%-33%, respectively, compared with placebo in non-relapsing patients. In the hypothetical scenarios, risk reductions independent of relapses were 14%-18% and 23% for 3- and 6-month confirmed disability progression, respectively.ConclusionBy controlling the confounding impact of on-study relapses on confirmed disability progression, these statistical approaches provide a methodological framework to assess treatment effects on disability progression in relapsing and non-relapsing patients. The analyses support that siponimod may be useful for treating secondary progressive multiple sclerosis in patients with or without relapses.

Graphics are at the core of exploring and understanding data, communicating results and conclusions, and supporting decision‐making. Increasing our graphical expertise can significantly strengthen our impact as professional statisticians and quantitative scientists. In this article, we present a concerted effort to improve the way we create graphics at Novartis. We provide our vision and guiding principles, before describing seven work packages in more detail. The actions, principles, and experiences laid out in this paper are applicable generally, also beyond drug development, which is our field of work. The purpose of this article is to share our experiences and help foster the use of good graphs in pharmaceutical statistics and beyond. A Graphics Principles "Cheat Sheet" is available online at https://graphicsprinciples.github.io/. [ABSTRACT FROM AUTHOR]

In many clinical trials, biological, pharmacological, or clinical information is used to define candidate subgroups of patients that might have a differential treatment effect. Once the trial results are available, interest will focus on subgroups with an increased treatment effect. Estimating a treatment effect for these groups, together with an adequate uncertainty statement is challenging, owing to the resulting 'random high' / selection bias. In this paper, we will investigate Bayesian model averaging to address this problem. The general motivation for the use of model averaging is to realize that subgroup selection can be viewed as model selection, so that methods to deal with model selection uncertainty, such as model averaging, can be used also in this setting. Simulations are used to evaluate the performance of the proposed approach. We illustrate it on an example early-phase clinical trial. [ABSTRACT FROM AUTHOR]

Background: In multiple sclerosis, impact of treatment on disability progression can be confounded if treatment also reduces relapses., Objective: To distinguish siponimod's direct effects on disability progression from those on relapses in the EXPAND phase 3 trial., Methods: Three estimands, one based on principal stratum and two on hypothetical scenarios (no relapses, or equal relapses in both treatment arms), were defined to determine the extent to which siponimod's effects on 3- and 6-month confirmed disability progression were independent of on-study relapses., Results: Principal stratum analysis estimated that siponimod reduced the risk of 3- and 6-month confirmed disability progression by 14%-20% and 29%-33%, respectively, compared with placebo in non-relapsing patients. In the hypothetical scenarios, risk reductions independent of relapses were 14%-18% and 23% for 3- and 6-month confirmed disability progression, respectively., Conclusion: By controlling the confounding impact of on-study relapses on confirmed disability progression, these statistical approaches provide a methodological framework to assess treatment effects on disability progression in relapsing and non-relapsing patients. The analyses support that siponimod may be useful for treating secondary progressive multiple sclerosis in patients with or without relapses.