{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Université Paris Cité et Université Sorbonne Paris Nord, Inserm, IAME, F-75018 Paris, France"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "N. Hendrickx"
    },
    {
      "name": "F. Mentre"
    },
    {
      "name": "M. O. Karlsson"
    },
    {
      "name": "A. C. Hooker"
    },
    {
      "name": "A. Traschütz"
    },
    {
      "name": "R. Schüle"
    },
    {
      "name": "PROSPAX Consortium"
    },
    {
      "name": "EVIDENCE-RND Consortium"
    },
    {
      "name": "M. Synofzik"
    },
    {
      "name": "E. Comets"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cpkcfqx8tmkg8.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.28.26361588",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.08.28.26361588v1.full",
  "id": "cpkcfqx8tmkg8",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.08.28.26361588v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "2613|Statistical Methods in Clinical Trials"
    ]
  ],
  "openalex": "W7206217188",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.08.28.26361588v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cpkcfqx8tmkg8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:31Z",
      "native_id": "10.64898/2026.08.28.26361588",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.08.28.26361588v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "New tests for trials of very few patients using longitudinal data - a case-study in Autosomal Recessive Cerebellar Ataxias",
  "title_text": "New tests for trials of very few patients using longitudinal data - a case-study in Autosomal Recessive Cerebellar Ataxias",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:31Z",
  "url": "https://research.draconitic.com/paper/cpkcfqx8tmkg8",
  "venue": "medRxiv",
  "versions": []
}