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It is highly desirable to borrow information from external data to augment acontrol arm in a randomized clinical trial, especially in settings where thesample size for the control arm is limited. However, a main challenge inborrowing information from external data is to accommodate potentialheterogeneous subpopulations across the external and trial data. We apply aBayesian nonparametric model called Plaid Atoms Model (PAM) to identifyoverlapping and unique subpopulations across datasets, with which we restrictthe information borrowing to the common subpopulations. This forms a hybridcontrol (HC) that leads to more precise estimation of treatment effectsSimulation studies demonstrate the robustness of the new method, and anapplication to an Atopic Dermatitis dataset shows improved treatment effectestimation.

PAM-HC: A Bayesian Nonparametric Construction of Hybrid Control for Randomized Clinical Trials Using External Data