ScholarWorks@성균관대학교

초록

Driven by evolving Food and Drug Administration recommendations, modern clinical trials demand innovative designs that strike a balance between statistical rigor and ethical considerations. Covariate-adjusted response-adaptive randomization (CARA) designs bridge this gap by utilizing patient attributes and responses to skew treatment allocation in favor of the treatment to be best for an individual patient's profiles. However, existing CARA designs for survival outcomes often rely on specific parametric models, constraining their applicability in clinical practice. To overcome this limitation, we propose a novel CARA method for survival outcomes (called CARAS) based on the Cox model, which improves model flexibility and mitigate risks of model misspecification. Additionally, we introduce a group sequential overlap-weighted log-rank test to preserve the type I error rate in group sequential trials using CARAS. Comprehensive simulation studies and a real-world trial example demonstrate the proposed method's clinical benefit, statistical efficiency, and robustness to model misspecification compared to traditional randomized controlled trial designs and response-adaptive randomization designs.

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