Home > Publications Database > Comparing randomized trial designs to estimate treatment effect in rare diseases with longitudinal models: a simulation study showcased by Autosomal Recessive Cerebellar Ataxias using the SARA score. > print

001     280242
005     20250827101637.0
024 7 _ |a 10.1186/s12874-025-02626-x
|2 doi
024 7 _ |a pmid:40739200
|2 pmid
024 7 _ |a pmc:PMC12309037
|2 pmc
037 _ _ |a DZNE-2025-00920
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Hendrickx, Niels
|b 0
245 _ _ |a Comparing randomized trial designs to estimate treatment effect in rare diseases with longitudinal models: a simulation study showcased by Autosomal Recessive Cerebellar Ataxias using the SARA score.
260 _ _ |a London
|c 2025
|b BioMed Central
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1754895766_31916
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a 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.
536 _ _ |a 353 - Clinical and Health Care Research (POF4-353)
|0 G:(DE-HGF)POF4-353
|c POF4-353
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: pub.dzne.de
650 _ 7 |a Clinical trial design
|2 Other
650 _ 7 |a Model-based analysis
|2 Other
650 _ 7 |a Non-linear Mixed effect models
|2 Other
650 _ 7 |a Rare disease
|2 Other
650 _ 7 |a Simulation study
|2 Other
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Randomized Controlled Trials as Topic: methods
|2 MeSH
650 _ 2 |a Cerebellar Ataxia: therapy
|2 MeSH
650 _ 2 |a Cerebellar Ataxia: genetics
|2 MeSH
650 _ 2 |a Computer Simulation
|2 MeSH
650 _ 2 |a Longitudinal Studies
|2 MeSH
650 _ 2 |a Disease Progression
|2 MeSH
650 _ 2 |a Research Design
|2 MeSH
650 _ 2 |a Rare Diseases: therapy
|2 MeSH
650 _ 2 |a Treatment Outcome
|2 MeSH
700 1 _ |a Mentré, France
|b 1
700 1 _ |a Hamdan, Alzahra
|b 2
700 1 _ |a Karlsson, Mats O
|b 3
700 1 _ |a Hooker, Andrew C
|b 4
700 1 _ |a Traschütz, Andreas
|0 P:(DE-2719)9000792
|b 5
|u dzne
700 1 _ |a Gagnon, Cynthia
|b 6
700 1 _ |a Schüle-Freyer, Rebecca
|0 P:(DE-2719)2812018
|b 7
|u dzne
700 1 _ |a Synofzik, Matthis
|0 P:(DE-2719)2811275
|b 8
|e Last author
|u dzne
700 1 _ |a Comets, Emmanuelle
|b 9
700 1 _ |a ARCA Study Group, EVIDENCE-RND consortium
|b 10
|e Collaboration Author
700 1 _ |a Chen, Xiaomei
|b 11
|e Contributor
700 1 _ |a Heussen, Nicole Maria
|b 12
|e Contributor
700 1 _ |a Hilgers, Ralf-Dieter
|b 13
|e Contributor
700 1 _ |a Klockgether, Thomas
|0 P:(DE-2719)2810314
|b 14
|e Contributor
|u dzne
700 1 _ |a Ryeznik, Yevgen
|b 15
|e Contributor
700 1 _ |a Sverdlov, Oleksandr
|b 16
|e Contributor
773 _ _ |a 10.1186/s12874-025-02626-x
|g Vol. 25, no. 1, p. 179
|0 PERI:(DE-600)2041362-2
|n 1
|p 179
|t BMC medical research methodology
|v 25
|y 2025
|x 1471-2288
856 4 _ |y OpenAccess
|u https://pub.dzne.de/record/280242/files/DZNE-2025-00920.pdf
856 4 _ |u https://pub.dzne.de/record/280242/files/DZNE-2025-00920%20SUP.docx
856 4 _ |u https://pub.dzne.de/record/280242/files/DZNE-2025-00920.pdf?subformat=pdfa
|x pdfa
|y OpenAccess
909 C O |o oai:pub.dzne.de:280242
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Deutsches Zentrum für Neurodegenerative Erkrankungen
|0 I:(DE-588)1065079516
|k DZNE
|b 5
|6 P:(DE-2719)9000792
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 7
|6 P:(DE-2719)2812018
910 1 _ |a Deutsches Zentrum für Neurodegenerative Erkrankungen
|0 I:(DE-588)1065079516
|k DZNE
|b 8
|6 P:(DE-2719)2811275
910 1 _ |a External Institute
|0 I:(DE-HGF)0
|k Extern
|b 14
|6 P:(DE-2719)2810314
913 1 _ |a DE-HGF
|b Gesundheit
|l Neurodegenerative Diseases
|1 G:(DE-HGF)POF4-350
|0 G:(DE-HGF)POF4-353
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Clinical and Health Care Research
|x 0
914 1 _ |y 2025
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2024-12-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2024-12-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2024-12-18
915 _ _ |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0
|0 LIC:(DE-HGF)CCBYNCND4
|2 HGFVOC
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b BMC MED RES METHODOL : 2022
|d 2024-12-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2024-04-10T15:34:47Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2024-04-10T15:34:47Z
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2024-12-18
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2024-12-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2024-12-18
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2024-12-18
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2024-12-18
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2024-12-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2024-12-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2024-12-18
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2024-12-18
920 1 _ |0 I:(DE-2719)1210000
|k AG Gasser
|l Parkinson Genetics
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-2719)1210000
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21