Fanni Zhang, Kristine Broglio, Michael Sweeting, Gina D’Angelo
Dose optimization in oncology clinical trials has shifted from solely seeking the maximum tolerated dose to identifying the Optimal Biological Dose (OBD) that balances therapeutic benefits and risks across multiple clinical attributes. Existing advanced dose-finding methods can integrate multiple endpoints but may not be suitable to compare dose levels from randomized dose optimization studies with small sample sizes. To address these challenges, we propose a clinical utility index (CUI) based analysis and decision framework for dose optimization in multiple-dose, multiple-outcome randomized trials (CUI-MET). This framework integrates multiple binary endpoints into a combined CUI for each dose level by weighting together multiple endpoints. Marginal summaries of individual endpoints can be estimated either empirically or via one or more choices of different parametric dose-response models. These estimated probabilities are then combined using endpoint-specific weights to compute a utility score for each dose. The dose with the highest score within a prespecified clinically acceptable dose set is selected as optimal. Bootstrap analysis provides confidence intervals for the CUI and estimates the probability that each dose is selected as optimal, thereby evaluating the robustness of dose selection. To enhance usability, we implemented these methods in an interactive R Shiny application and demonstrated functionality through case examples. The framework's flexibility allows for different model selections and endpoint weighting schemes to reflect specific clinical priorities and sensitivity analyses. By integrating multiple endpoints into a single utility index and incorporating user-friendly visualizations, CUI-MET offers a flexible and accessible solution for dose optimization in early-phase oncology trials, supporting informed decision-making and the integration of patient-relevant outcomes.