Informing the Borrowing Process for Dose-Finding Trials by Estimating the Similarity Between Population-Specific Dose-Toxicity Curves.

The conduct of dose-finding trials can be specifically challenging in small populations, for example, in pediatric settings. Recently, research has shown that Bayesian borrowing from adult trials combined with appropriately robust prior distributions enables the conduct of pediatric dose-finding trials with very small sample sizes. However, the appropriate degree of borrowing remains a subjective choice, relying on default methods or expert opinion. This paper proposes an approach to empirically determine the degree of borrowing based on a meta-analysis of the similarity between population-specific dose-toxicity curves of other biologically similar compounds. Focusing on the pediatric use case, the approach may be applicable to any dose-finding trial with information borrowing from another population. With the ExNex and a hierarchical model, two popular statistical modeling approaches are applied. The estimated degree of similarity is then translated into the statistical model for the dose-finding algorithm using either variance inflation or robust mixture prior distributions. The performance of each combination of statistical model approaches is investigated in a simulation study. The results with mixture priors are promising for the application of the proposed methods, especially with many (20) compounds, while variance inflation models require additional fine-tuning and seem to be less robust. With fewer (3 or 7) compounds, our proposed methods are either in line with robust priors that ignore the data from other compounds or are slightly better. We further provide a case study analyzing real dose-finding data from 6 compounds with our models, demonstrating applicability in real-world situations. For clinical trials teams, the decision for or against the proposed approach might be connected to the efforts in terms of time and cost to receive the external data.