Title: Assessment of type I error rate associated with dose-group switching in a longitudinal Alzheimer trial
Authors: Habteab Ghebretinsae, Aklilu ×
Molenberghs, Geert
Dmitrienko, Alex
Offen, Walt
Sethuraman, Gopalan #
Issue Date: 2014
Publisher: Marcel Dekker
Series Title: Journal of Biopharmaceutical Statistics vol:24 issue:3 pages:660-684
Article number: 10.1080/10543406.2014.888570
Abstract: In clinical trials, there always is the possibility to use data-driven adaptation at the end of a study. There prevails, however, concern on whether the type I error rate of the trial could be inflated with such design, thus, necessitating multiplicity adjustment. In this project, a simulation experiment was set up to assess type I error rate inflation associated with switching dose group as a function of dropout rate at the end of the study, where the primary analysis is in terms of a longitudinal outcome. This simulation is inspired by a clinical trial in Alzheimer's disease. The type I error rate was assessed under a number of scenarios, in terms of differing correlations between efficacy and tolerance, different missingness mechanisms, and different probabilities of switching. A collection of parameter values was used to assess sensitivity of the analysis. Results from ignorable likelihood analysis show that the type I error rate with and without switching was approximately the posited error rate for the various scenarios. Under last observation carried forward (LOCF), the type I error rate blew up both with and without switching. The type I error inflation is clearly connected to the criterion used for switching. While in general switching, in a way related to the primary endpoint, may impact the type I error, this was not the case for most scenarios in the longitudinal Alzheimer trial setting under consideration, where patients are expected to worsen over time.
ISSN: 1054-3406
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat)
× corresponding author
# (joint) last author

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