New tests for trials of very few patients using longitudinal data - a case-study in Autosomal Recessive Cerebellar Ataxias
Year: 2026
Type: preprint
Abstract: We propose two new tests to detect drug effects (DE) in trials of one to very few patients followed during two periods (before and after initiation of a treatment). Both methods use longitudinal natural history data to inform the estimation of each patient’s DE. The first method uses a non-linear mixed effect model (NLMEM) reflecting an expected natural history with a hypothetical drug effect, to estimate the Conditional Distribution of the Drug Effect (CDDE). The second method trains a Pareto Depth Analysis (PDA) algorithm, a machine learning–based approach based on outlier detection, that we implement using data simulated under the NLMEM. We evaluated the two tests with a simulation study. We used data from the PROSPAX study in Autosomal Recessive Cerebellar Ataxias (ARCAs, to derive a NLMEM for the Scale for the Assessment and Rating of Ataxia score. The CDDE method provided controlled type I error and, in some scenarios, adequate corrected power, though sensitivity analyses showed vulnerability to misspecification. The PDA method demonstrated lower statistical power except with high score precision. These results highlight different strategies for quantifying treatment effects in ultra-rare, patient-specific trials. They can inform methodological design for future ARCA precision therapies.
Source: medRxiv
Institutions: Inserm, Université Paris Cité, Université Sorbonne Paris Nord, Infection, Anti-microbiens, Modélisation, Evolution, Uppsala University, German Center for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Heidelberg University, University Hospital Heidelberg, École des Hautes Études en Santé Publique, Institut de Recherche en Santé, Environnement et Travail, Université de Rennes
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: G1297096486, G1920181826, G4260212813, TreatHSP - Translationale Forschung für Hereditäre Spastische Spinalparalysen: Projektkoordination, zentrale Infrastruktur und Entwicklung einer Adaptiven Outcome-Validierungsplatform, Entwicklung einer Antisense-basierten RNA-Therapie für die POLR3A-assoziierte HSP, G6749461845, EUROPEAN RARE DISEASES RESEARCH ALLIANCE, European Joint Programme on Rare Diseases
OpenAlex ID: W7206217188