Improving early clinical trial phase identification of promising therapeutics

Abstract

This review addresses decision-making underlying the frequent failure to confirm early-phase positive trial results and how to prioritize which early agents to transition to late phase. While unexpected toxicity is sometimes responsible for late-phase failures, lack of efficacy is also frequently found. In stroke as in other conditions, early trials often demonstrate imbalances in factors influencing outcome. Other issues complicate early trial analysis, including unequally distributed noise inherent in outcome measures and variations in natural history among studies. We contend that statistical approaches to correct for imbalances and noise, while likely valid for homogeneous conditions, appear unable to accommodate disease complexity and have failed to correctly identify effective agents. While blinding and randomization are important to reduce selection bias, these methods appear insufficient to insure valid conclusions. We found potential sources of analytical errors in nearly 90% of a sample of early stroke trials. To address these issues, we recommend changes in early-phase analysis and reporting: (1) restrict use of statistical correction to studies where the underlying assumptions are validated, (2) select dichotomous over continuous outcomes for small samples, (3) consider pooled samples to model natural history to detect early therapeutic signals and increase the likelihood of replication in larger samples, (4) report subgroup baseline conditions, (5) consider post hoc methods to restrict analysis to subjects with an appropriate match, and (6) increase the strength of effect threshold given these cumulative sources of noise and potential errors. More attention to these issues should lead to better decision-making regarding selection of agents to proceed to pivotal trials.