Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Ergebnis 9 von 12
Annals of neurology, 2023-09, Vol.94 (3), p.547-560
2023
Volltextzugriff (PDF)

Details

Autor(en) / Beteiligte
Titel
Design and Statistical Innovations in a Platform Trial for Amyotrophic Lateral Sclerosis
Ist Teil von
  • Annals of neurology, 2023-09, Vol.94 (3), p.547-560
Ort / Verlag
Hoboken, USA: John Wiley & Sons, Inc
Erscheinungsjahr
2023
Quelle
Wiley Online Library All Journals
Beschreibungen/Notizen
  • Platform trials allow efficient evaluation of multiple interventions for a specific disease. The HEALEY ALS Platform Trial is testing multiple investigational products in parallel and sequentially in persons with amyotrophic lateral sclerosis (ALS) with the goal of rapidly identifying novel treatments to slow disease progression. Platform trials have considerable operational and statistical efficiencies compared with typical randomized controlled trials due to their use of shared infrastructure and shared control data. We describe the statistical approaches required to achieve the objectives of a platform trial in the context of ALS. This includes following regulatory guidance for the disease area of interest and accounting for potential differences in outcomes of participants within the shared control (potentially due to differences in time of randomization, mode of administration, and eligibility criteria). Within the HEALEY ALS Platform Trial, the complex statistical objectives are met using a Bayesian shared parameter analysis of function and survival. This analysis serves to provide a common integrated estimate of treatment benefit, overall slowing in disease progression, as measured by function and survival while accounting for potential differences in the shared control group using Bayesian hierarchical modeling. Clinical trial simulation is used to provide a better understanding of this novel analysis method and complex design. ANN NEUROL 2023;94:547–560
Sprache
Englisch
Identifikatoren
ISSN: 0364-5134
eISSN: 1531-8249
DOI: 10.1002/ana.26714
Titel-ID: cdi_proquest_miscellaneous_2820026915
Format

Weiterführende Literatur

Empfehlungen zum selben Thema automatisch vorgeschlagen von bX