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PreprintCC BY-NC-NDmedRxiv

New tests for trials of very few patients using longitudinal data - a case-study in Autosomal Recessive Cerebellar Ataxias

  • N. Hendrickx1
  • F. Mentre
  • M. O. Karlsson
  • A. C. Hooker
  • A. Traschütz
  • R. Schüle
  • PROSPAX Consortium
  • EVIDENCE-RND Consortium
  • M. Synofzik
  • E. Comets
  1. 1Université Paris Cité et Université Sorbonne Paris Nord, Inserm, IAME, F-75018 Paris, France
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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 patients 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.

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Cite

BibTeXRIS
BibTeX
@misc{hendrickx2026tests,
  title = {{New tests for trials of very few patients using longitudinal data - a case-study in Autosomal Recessive Cerebellar Ataxias}},
  author = {N. Hendrickx and F. Mentre and M. O. Karlsson and A. C. Hooker and A. Traschütz and R. Schüle and PROSPAX Consortium and EVIDENCE-RND Consortium and M. Synofzik and E. Comets},
  howpublished = {medRxiv},
  year = {2026},
  month = aug,
  doi = {10.64898/2026.08.28.26361588},
  url = {https://www.medrxiv.org/content/10.64898/2026.08.28.26361588v1},
  note = {CC BY-NC-ND},
}
RIS
TY  - UNPB
TI  - New tests for trials of very few patients using longitudinal data - a case-study in Autosomal Recessive Cerebellar Ataxias
AU  - N. Hendrickx
AU  - F. Mentre
AU  - M. O. Karlsson
AU  - A. C. Hooker
AU  - A. Traschütz
AU  - R. Schüle
AU  - PROSPAX Consortium
AU  - EVIDENCE-RND Consortium
AU  - M. Synofzik
AU  - E. Comets
PY  - 2026
DA  - 2026/08/28
PB  - medRxiv
DO  - 10.64898/2026.08.28.26361588
UR  - https://www.medrxiv.org/content/10.64898/2026.08.28.26361588v1
AB  - 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 patients 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.
ER  -