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@ARTICLE{Hendrickx:280242,
      author       = {Hendrickx, Niels and Mentré, France and Hamdan, Alzahra
                      and Karlsson, Mats O and Hooker, Andrew C and Traschütz,
                      Andreas and Gagnon, Cynthia and Schüle-Freyer, Rebecca and
                      Synofzik, Matthis and Comets, Emmanuelle},
      collaboration = {ARCA Study Group, EVIDENCE-RND consortium},
      othercontributors = {Chen, Xiaomei and Heussen, Nicole Maria and Hilgers,
                          Ralf-Dieter and Klockgether, Thomas and Ryeznik, Yevgen and
                          Sverdlov, Oleksandr},
      title        = {{C}omparing randomized trial designs to estimate treatment
                      effect in rare diseases with longitudinal models: a
                      simulation study showcased by {A}utosomal {R}ecessive
                      {C}erebellar {A}taxias using the {SARA} score.},
      journal      = {BMC medical research methodology},
      volume       = {25},
      number       = {1},
      issn         = {1471-2288},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {DZNE-2025-00920},
      pages        = {179},
      year         = {2025},
      abstract     = {Parallel designs with an end-of-treatment analysis are
                      commonly used for randomised trials, but they remain
                      challenging to conduct in rare diseases due to small sample
                      size and heterogeneity. A more powerful alternative could be
                      to use model-based approaches. We investigated the
                      performance of longitudinal modelling to evaluate
                      disease-modifying treatments in rare diseases using
                      simulations. Our setting was based on a model describing the
                      progression of the standard clinician-reported outcome SARA
                      score in patients with ARCA (Autosomal Recessive Cerebellar
                      Ataxia), a group of ultra-rare, genetically defined,
                      neurodegenerative diseases. We performed a simulation study
                      to evaluate the influence of trials settings on their
                      ability to detect a treatment effect slowing disease
                      progression, using a previously published non-linear mixed
                      effect logistic model. We compared the power of parallel,
                      crossover and delayed start designs, investigating several
                      trial settings: trial duration (2 or 5 years); disease
                      progression rate (slower or faster); magnitude of residual
                      error (σ=2 or σ=0.5); number of patients (100 or 40);
                      method of statistical analysis (longitudinal analysis with
                      non-linear or linear models; standard statistical analysis),
                      and we investigated their influence on the type 1 error and
                      corrected power of randomised trials. In all settings, using
                      non-linear mixed effect models resulted in controlled type 1
                      error and higher power $(88\%$ for a parallel design) than a
                      rich $(75\%$ for a parallel design) or sparse $(49\%$ for a
                      parallel design) linear mixed effect model or standard
                      statistical analysis $(36\%$ for a parallel design).
                      Parallel and delayed start designs performed better than
                      crossover designs. With slow disease progression and high
                      residual error, longer durations are needed for power to be
                      greater than $80\%,$ 5 years for slower progression and 2
                      years for faster progression ataxias. In our settings, using
                      non-linear mixed effect modelling allowed all three designs
                      to have more power than a standard end-of-treatment
                      analysis. Our analysis also showed that delayed start
                      designs are promising as, in this context, they are as
                      powerful as parallel designs, but with the advantage that
                      all patients are treated within this design.},
      keywords     = {Humans / Randomized Controlled Trials as Topic: methods /
                      Cerebellar Ataxia: therapy / Cerebellar Ataxia: genetics /
                      Computer Simulation / Longitudinal Studies / Disease
                      Progression / Research Design / Rare Diseases: therapy /
                      Treatment Outcome / Clinical trial design (Other) /
                      Model-based analysis (Other) / Non-linear Mixed effect
                      models (Other) / Rare disease (Other) / Simulation study
                      (Other)},
      cin          = {AG Gasser},
      ddc          = {610},
      cid          = {I:(DE-2719)1210000},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40739200},
      pmc          = {pmc:PMC12309037},
      doi          = {10.1186/s12874-025-02626-x},
      url          = {https://pub.dzne.de/record/280242},
}