Proof of concept demonstration of optimal composite MRI endpoints for clinical trials

Abstract

BACKGROUND Atrophy measures derived from structural MRI are promising outcome measures for early phase clinical trials, especially for rare diseases such as primary progressive aphasia (PPA), where the small available subject pool limits our ability to perform meaningfully powered trials with traditional cognitive and functional outcome measures. METHODS We investigated a composite atrophy index in 26 PPA participants with longitudinal MRIs separated by two years. Rogalski et al. [Neurology 2014;83:1184-1191] previously demonstrated that atrophy of the left perisylvian temporal cortex (PSTC) is a highly sensitive measure of disease progression in this population and a promising endpoint for clinical trials. Using methods described by Ard et al. [Pharmaceutical Statistics 2015;14:418-426], we constructed a composite atrophy index composed of a weighted sum of volumetric measures of 10 regions of interest within the left perisylvian cortex using weights that maximize signal-to-noise and minimize sample size required of trials using the resulting score. Sample size required to detect a fixed percentage slowing in atrophy in a two-year clinical trial with equal allocation of subjects across arms and 90% power was calculated for the PSTC and optimal composite surrogate biomarker endpoints. RESULTS The optimal composite endpoint required 38% fewer subjects to detect the same percent slowing in atrophy than required by the left PSTC endpoint. CONCLUSIONS Optimal composites can increase the power of clinical trials and increase the probability that smaller trials are informative, an observation especially relevant for PPA, but also for related neurodegenerative disorders including Alzheimer's disease.

DOI: 10.1016/j.trci.2016.05.002

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@inproceedings{Edland2016ProofOC, title={Proof of concept demonstration of optimal composite MRI endpoints for clinical trials}, author={Steven Edland and M. Colin Ard and Jaiashre Sridhar and Derin J. Cobia and Adam Martersteck and M.-Marsel Mesulam and Emily J. Rogalski}, booktitle={Alzheimer's & dementia}, year={2016} }