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
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...
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2025-07-01
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| Online Access: | https://doi.org/10.1186/s12874-025-02626-x |
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| author | Niels Hendrickx France Mentré Alzahra Hamdan Mats O. Karlsson Andrew C. Hooker Andreas Traschütz Cynthia Gagnon Rebecca Schüle Matthis Synofzik Emmanuelle Comets ARCA Study Group, EVIDENCE-RND consortium |
| author_facet | Niels Hendrickx France Mentré Alzahra Hamdan Mats O. Karlsson Andrew C. Hooker Andreas Traschütz Cynthia Gagnon Rebecca Schüle Matthis Synofzik Emmanuelle Comets ARCA Study Group, EVIDENCE-RND consortium |
| author_sort | Niels Hendrickx |
| collection | DOAJ |
| description | 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 ( $$\sigma$$ =2 or $$\sigma$$ =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. |
| format | Article |
| id | doaj-art-4323b427b2ca49678049708295c650cd |
| institution | Kabale University |
| issn | 1471-2288 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
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| series | BMC Medical Research Methodology |
| spelling | doaj-art-4323b427b2ca49678049708295c650cd2025-08-20T03:46:09ZengBMCBMC Medical Research Methodology1471-22882025-07-0125111510.1186/s12874-025-02626-xComparing 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 scoreNiels Hendrickx0France Mentré1Alzahra Hamdan2Mats O. Karlsson3Andrew C. Hooker4Andreas Traschütz5Cynthia Gagnon6Rebecca Schüle7Matthis Synofzik8Emmanuelle Comets9ARCA Study Group, EVIDENCE-RND consortiumUniversité Paris Cité, IAME, InsermUniversité Paris Cité, IAME, InsermDepartment of Pharmacy, Pharmacometrics Research Group, Uppsala UniversityDepartment of Pharmacy, Pharmacometrics Research Group, Uppsala UniversityDepartment of Pharmacy, Pharmacometrics Research Group, Uppsala UniversityDivision Translational Genomics of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research (HIH), University of TübingenCentre de Recherche du CHUS Et du Centre de Santé Et Des Services, Sociaux du Saguenay-Lac-St-Jean, Faculté de Médecine, Université de SherbrookeDepartment of Neurology, Division of Neurodegenerative Diseases and Movement Disorders, Heidelberg University Hospital and Faculty of MedicineDivision Translational Genomics of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research (HIH), University of TübingenUniversité Paris Cité, IAME, InsermAbstract 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 ( $$\sigma$$ =2 or $$\sigma$$ =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.https://doi.org/10.1186/s12874-025-02626-xNon-linear Mixed effect modelsSimulation studyRare diseaseClinical trial designModel-based analysis |
| spellingShingle | Niels Hendrickx France Mentré Alzahra Hamdan Mats O. Karlsson Andrew C. Hooker Andreas Traschütz Cynthia Gagnon Rebecca Schüle Matthis Synofzik Emmanuelle Comets ARCA Study Group, EVIDENCE-RND consortium 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 BMC Medical Research Methodology Non-linear Mixed effect models Simulation study Rare disease Clinical trial design Model-based analysis |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Non-linear Mixed effect models Simulation study Rare disease Clinical trial design Model-based analysis |
| url | https://doi.org/10.1186/s12874-025-02626-x |
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