Sample size estimation for a flexible phase II oncology trial design using two dependent endpoints with nested response metrics.

Abstract

Phase II oncology trials are often designed using a single primary efficacy endpoint, even when multiple clinically relevant response metrics are available. When two endpoints are based on nested response metrics, such as objective response rate and disease control rate or progression-free survival at two ordered time points, success on the more stringent endpoint implies success on the broader endpoint. This structure induces a known dependence relationship that can be incorporated into sample size estimation and decision-rule construction. We develop a flexible phase II design framework for two primary endpoints with nested response metrics. The framework includes union-based designs for declaring activity on at least one endpoint, intersection-based designs for requiring activity on both endpoints, and multiplicity-adjusted designs for endpoint-specific inference with strong familywise error-rate control. For the multiplicity-adjusted setting, we propose Hochberg-type and Holm-type designs and provide sample size optimization algorithms based on the nested dependence structure. An application to a phase II trial of sintilimab in recurrent or progressive meningioma illustrates the approach using 12-month and 6-month progression-free survival. Compared with a Bonferroni design, the Holm-type and Hochberg-type designs can reduce the required sample size while maintaining prespecified error-rate and power requirements.