Statistical Considerations for Planning Clinical Trials with Quantitative Imaging Biomarkers

Abstract

As imaging technologies and treatment options continue to advance, imaging outcome measures are becoming increasingly utilized as the basis of making major decisions in new drug development and clinical practice. Quantitative imaging biomarkers (QIBs) are now commonly used for subject selection, response assessment, and safety monitoring. Although quantitative measurements can have many advantages compared with subjective, qualitative endpoints, it is important to recognize that QIBs are measured with error. This study uses Monte Carlo simulation to examine the impact of measurement error on a variety of clinical trial designs as well as to test proposed adjustments for measurement error. The focus is on some of the QIBs currently being studied by the Quantitative Imaging Biomarkers Alliance. The results show that the ability of QIBs to discriminate between health states and predict patient outcome is attenuated by measurement error; however, the known technical performance characteristics of QIBs can be used to adjust study sample size, control the misinterpretation rate of imaging findings, and establish statistically valid decision thresholds. We conclude that estimates of the precision and bias of a QIB are important for properly designing clinical trials and establishing the level of imaging standardization required.