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Center for Global Research Data

Bayesian probability of success for multivariate generalized linear models

Lead Investigator: Ethan Alt, University of North Carolina at Chapel Hill
Title of Proposal Research: Bayesian probability of success for multivariate generalized linear models
Vivli Data Request: 6521
Funding Source: None
Potential Conflicts of Interest: None

Summary of the Proposed Research:

The project will enable clinicians to power studies with possibly mixed outcome types (e.g., binary, continuous, and count). Because trials typically have primary and multiple secondary endpoints, methods to determine sample size to achieve clinical success across multiple outcomes are necessary. However, such methods are scarce. In earlier work, we have developed a Bayesian approach to multiplicity that is uniformly more powerful than frequentist adjustment procedures, such as the Holm-Bonferroni method, for multiple continuous outcomes (see https://arxiv.org/abs/2010.13774). In this work, we aim to extend this previous work to the case of the multivariate generalized linear model.

Requested Studies:

A Multicenter, Randomized, Double-Blind, Placebo-Controlled Exploratory Study to Assess the Effect of Treatment With Prolonged-Release Fampridine (BIIB041) 10 mg Twice Daily on Walking Ability and Balance in Subjects With Multiple Sclerosis
Sponsor: Biogen
Study ID: NCT01597297
Sponsor ID: 218MS205