Rethinking Clinical Trial Outcomes: Use of a Standardized Metric to Enhance Design and Interpretation

The use of a standardized outcome metric enhances clinical trial conduct, interpretation, and cross-trial comparison. If a disease course is predictable, comparing modeled predictions with outcome data affords the precision and confidence needed to accelerate precision medicine. We demonstrate the power of this approach in type 1 diabetes (T1D) trials aiming to preserve endogenous insulin secretion measured by C-peptide. C-peptide is a predictable outcome given an individual's age and baseline value; quantitative response (QR) adjusts for these variables and represents the difference between the observed and predicted outcome. Validated across 13 trials, the QR metric reduces each trial's variance and markedly increases statistical power. As smaller studies are especially subject to random sampling variability, using QR as the outcome metric introduces alternative interpretations of previous clinical trial results analyzed by traditional statistical methods. QR can provide model-based estimates that quantify whether individuals or groups did better or worse than expected. QR also provides a purer metric to associate with biomarker data, enabling improved mechanistic insights. Using data from more than 1,300 participants, we demonstrate the value of QR in advancement of disease-modifying therapy (DMT) in T1D. QR applies to any disease where outcome is predictable by baseline covariates, rendering it useful for defining responders to therapy, comparing the effectiveness of different therapies, and understanding causal pathways in disease.