Exploring synthetic controls in rare diseases with a proof of concept in spinal cord injury.

ABSTRACT:

Background

Successfully completing clinical trials for rare and heterogeneous disorders, like spinal cord injuries (SCI), remains challenging, thereby reducing the ability to test and translate promising preclinical findings. We propose synthetic controls, derived from data-driven predictions of recovery in patients undergoing standard treatments, to mitigate these challenges, in particular related to patient recruitment.

Methods

Based on data from the European Multicenter Study about Spinal Cord Injury (EMSCI) and the Sygen trial, we construct synthetic controls from personalized predictions of neurological recovery of sequences of segmental motor scores. A total of six architectures (linear, tree, and deep learning models) are compared. We demonstrate the applicability of

SUBMITTER: Lukas LP 

PROVIDER: S-EPMC12551237 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature