# How to improve statistical power in a trial with SCA2 patients

Maylis Tran, Pierre-Emmanuel Poulet, Cécile Di Folco, Giulia Coarelli, Alexandra Durr, Thomas Klockgether, Heike Jacobi, Tetsuo Ashizawa, Sophie Tezenas Du Montcel

► To cite this version:

Maylis Tran, Pierre-Emmanuel Poulet, Cécile Di Folco, Giulia Coarelli, Alexandra Durr, et al. How to improve statistical power in a trial with SCA2 patients. ICAR 2024 - International Congress for Ataxia Research, Nov 2024, London (U.K.), United Kingdom. hal-05234025

HAL Id: hal-05234025

https://hal.science/hal-05234025v1

Submitted on 1 Sep 2025

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# How to improve statistical power in a trial with SCA2 patients

Authors: Maylis Tran, Pierre-Emmanuel Poulet, Cécile di Folco, Giulia Coarelli, Alexandra Durr, Thomas Klockgether, Heike Jacobi, Tetsuo Ashizawa, Sophie Tezenas du Montcel

Sorbonne University, Paris Brain Institute, INSERM, CNRS, INRIA, Assistance Publique-Hôpitaux de Paris (APHP), University Hospital Pitié-Salpêtrière, Paris, France.

## Background and objectives:

ATRIL study showed that riluzole did not improve clinical outcomes (Coarelli et al., 2022). SARA scores showed indeed a median increase (ie, worsening) of 0.5 points (IQR -1.5;1.5) in the riluzole group versus 0.3 points (-1.0;2.5) in the placebo group (p=0.70). Our objective was to use recent advancements in statistical methodologies to improve the trial analysis.

## Methods:

SCA2 patients from different cohorts of the US and European centers were selected in a longitudinal dataset (EUROSCA, SPATAX, CRC-SCA, RISCA), to train a progression model capturing the natural disease development in untreated patients (Disease Course Mapping using Leaspy python library). Using the prognostic score predicted by this model, we applied prognostic covariate adjustment and prediction-powered inference (PPI++) to enhance the power of the ATRIL clinical trial, involving both treated and placebo SCA2 patients. In contrast, matching-adjusted indirect comparison (MAIC) enhances analysis by adjusting baseline characteristics between treatment groups rather than leveraging prognostic scores.

## Results:

Our progression model accurately forecasted prognostic scores for SCA2 patients, providing insights into disease progression trajectories. The variance of the average treatment effect decreased more with PPI++ than with prognostic covariate adjustment, in comparison to the classical method allowing a gain in power for the treatment comparison. Incorporating the prognostic score among the covariates allowed to reduce the sample size, while maintaining the results validity.

## Discussion and Conclusion:

Our study addresses the dual challenge of identifying the optimal longitudinal model for predicting disease progression and improving the conclusions of the ATRIL trial through statistical methods incorporating natural disease change predictions. The methods tested aim to reduce confidence intervals, improve power and decrease the required sample size for the clinical trial while maintaining the results validity. This approach holds promise for refining treatment efficacy assessments and facilitating informed clinical decisions in ataxia management.