30: Bias correction in treatment effect estimates following data-driven biomarker cutoff selection

Music City Center 

Predictive biomarkers play an essential role in precision medicine. Identifying an optimal cutoff to select patient subsets with greater benefit from treatment is critical and challenging for predictive biomarkers on a continuous scale. In early-stage studies, exploratory subset analyses are commonly used to select the cutoff. However, data-driven cutoff selection will often cause bias in treatment effect estimates and lead to over-optimistic expectations in the future phase III trial. In this study, we first conducted extensive simulations to investigate factors influencing the bias, including the cutoff selection rule, the number of candidates cutoffs, the magnitude of the predictive effect, and sample sizes. Our insights emphasize the need to consider bias and uncertainties from small sample sizes and data-driven selection in Go/No Go decision-making, and population and sample size planning for phase III studies. Secondly, we evaluated the performance of Bootstrap Bias Correction and the Approximate Bayesian Computation method for bias correction through simulations. We conclude by recommending the application of the two approaches in clinical practice.

Predictive Biomarker

Data-Driven Cutoff Selection

Estimation Bias

Subgroup Analyses

Bootstrap Bias Correction

Approximate Bayesian Computation 

Biopharmaceutical Section