The Locally Active‐Controlled Optimal Design: Applications in Oncology Clinical Studies

Antitumor activity in oncology clinical trials is typically assessed using overall survival (OS) or progression-free survival (PFS) endpoints, which are often imprecise and uninformative in small, noncomparative studies. The tumor growth inhibition (TGI) model, which captures both drug effects and natural tumor growth, quantitatively characterizes tumor size dynamics as a function of drug dosage, offering a more informative framework for comparing cancer treatments. In this work, we study the locally optimal design for a comparative oncology trial in which Dalpiciclib is the investigational agent and Capecitabine is the reference drug under an active control (AC) setting. Our novel approach avoids unrealistic distributional assumptions about response measurements. The resulting locally AC-optimal design minimizes the variance of the estimated matching dose of Dalpiciclib to Capecitabine and may unify Phase II and Phase III objectives by allowing evaluation of a higher Dalpiciclib dose with prespecified superiority.