UBC Theses and Dissertations
An adaptive clinical trial design for a sensitive subgroup examined in the multiple sclerosis context Riddell, Corinne Aileen
Adaptive clinical trials are recently gaining more attention. In this thesis, generalizations to the Biomarker-Adaptive Threshold Design (BATD) are studied and applied in the multiple sclerosis (MS) context. The BATD was originally developed for survival outcomes for Phase III clinical trials and allows researchers to both study the efficacy of treatment in the overall group and to investigate the relationship between a hypothesized predictive biomarker and the treatment effect on the primary outcome. We first introduce the original methodology and replicate the authors’ simulation studies to confirm their findings. Then, we generalize the methodology to accommodate count biomarkers and outcomes. Our interest in variables of this form is fuelled by the study of MS, where the number of relapses is a commonly used count outcome for patients with relapsing-remitting MS. Through simulation studies, we find that the BATD has increased power compared with a traditional fixed design under varying scenarios for which there exists a sensitive patient subgroup. As an illustrative example, we consider data from a previously completed trial and apply the methodology for two hypothesized markers: baseline lesion activity and the length of time that a patient has had MS. While we do not find a predictive biomarker relationship between baseline lesion activity and the number of relapses, MS duration does appear to have a predictive biomarker relationship for this dataset. In particular, we consider a randomly chosen subsample of the data for which the overall treatment effect on the outcome was insignificant. When the BATD is applied, a very significant treatment effect is detected and indicates that the effect is strongest for patients that have had MS for less than 7.8 years for this subsample. The methodology holds promise at preserving statistical power when the treatment effect is greatest in a sensitive patient subset.
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