Efficiency of multivariate tests in trials in progressive supranuclear palsy.

Clinical trials Item response theory Multiple endpoints Multivariate tests Progressive supranuclear palsy Simulation study

Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
26 Oct 2024
Historique:
received: 12 12 2023
accepted: 15 10 2024
medline: 27 10 2024
pubmed: 27 10 2024
entrez: 27 10 2024
Statut: epublish

Résumé

Measuring disease progression in clinical trials for testing novel treatments for multifaceted diseases as progressive supranuclear palsy (PSP), remains challenging. In this study we assess a range of statistical approaches to compare outcomes as measured by the items of the progressive supranuclear palsy rating scale (PSPRS). We consider several statistical approaches, including sum scores, a modified PSPRS rating scale that had been recommended by FDA in a pre-IND meeting, multivariate tests, and analysis approaches based on multiple comparisons of the individual items. In addition, we propose two novel approaches which measure disease status based on Item Response Theory models. We assess the performance of these tests under various scenarios in an extensive simulation study and illustrate their use with a re-analysis of the ABBV-8E12 clinical trial. Furthermore, we discuss the impact of the FDA-recommended scoring of item scores on the power of the statistical tests. We find that classical approaches as the PSPRS sum score demonstrate moderate to high power when treatment effects are consistent across the individual items. The tests based on Item Response Theory (IRT) models yield the highest power when the simulated data are generated from an IRT model. The multiple testing based approaches have a higher power in settings where the treatment effect is limited to certain domains or items. The study demonstrates that there is no one-size-fits-all testing procedure for evaluating treatment effects using PSPRS items; the optimal method varies based on the specific effect size patterns. The efficiency of the PSPRS sum score, while generally robust and straightforward to apply, varies depending on the specific patterns of effect sizes encountered and more powerful alternatives are available in specific settings. These findings can have important implications for the design of future clinical trials in PSP and similar multifaceted diseases.

Identifiants

pubmed: 39462124
doi: 10.1038/s41598-024-76668-4
pii: 10.1038/s41598-024-76668-4
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

25581

Subventions

Organisme : European Joint Programme on Rare Diseases
ID : Improve-PSP
Organisme : German Federal Ministry of Education and Research
ID : BMBF: 01KU1403A EpiPD
Organisme : German Federal Ministry of Education and Research
ID : 01EK1605A HitTau
Organisme : Deutsche Forschungsgemeinschaft
ID : EXC 2145 SyNergy - ID 390857198
Organisme : DFG grants
ID : HO2402/6-2
Organisme : DFG grants
ID : HO2402/18-1 MSAomics

Informations de copyright

© 2024. The Author(s).

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Auteurs

Elham Yousefi (E)

Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.

Mohamed Gewily (M)

Department of Pharmacy, Uppsala University, Uppsala, Sweden.

Franz König (F)

Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.

Günter Höglinger (G)

Department of Neurology, LMU University Hospital, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany.
German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
Department of Neurology, Hannover Medical School, Hanover, Germany.

Franziska Hopfner (F)

Department of Neurology, LMU University Hospital, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany.

Mats O Karlsson (MO)

Department of Pharmacy, Uppsala University, Uppsala, Sweden.

Robin Ristl (R)

Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.

Sonja Zehetmayer (S)

Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.

Martin Posch (M)

Center for Medical Data Science, Medical University of Vienna, Vienna, Austria. martin.posch@meduniwien.ac.at.

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