Bayesian designs to account for patient heterogeneity in phase II clinical trials

Between-patient heterogeneity is very common in clinical trials. This complicates treatment evaluation, due to known prognostic subgroup effects or potential treatment-subgroup interactions. We review two Bayesian phase II clinical trial designs that account explicitly for patient heterogeneity. The first design uses analysis of covariance to assess treatment effects in subgroups known to have different prognoses. The second design uses a hierarchical model for settings where, a priori, the experimental treatment effects in the subgroups are assumed to be exchangeable.

Compared with simpler designs that ignore patient heterogeneity, each design provides substantial improvements by reducing both false positive and false negative rates and focusing resources on subgroups where an experimental treatment is more likely to provide an advance over standard therapy. In either case, accounting for potential treatment-subgroup interactions is extremely important.

Due to the rapidly increasing number of potential new treatments to be evaluated clinically, increasing costs and limited resources, it is critically important to perform early phase clinical trials efficiently. The new Bayesian methods described here and other related methods provide efficient, broadly applicable tools to address these problems.