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Register a new userBayesian local exchangeability design for phase II basket trials
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Yilin Liu
Publication date
2021
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Mathematical Statistics
and research's language is
English
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We propose an information borrowing strategy for the design and monitoring of phase II basket trials based on the local multisource exchangeability assumption between baskets (disease types). We construct a flexible statistical design using the proposed strategy. Our approach partitions potentially heterogeneous baskets into non-exchangeable blocks. Information borrowing is only allowed to occur locally, i.e., among similar baskets within the same block. The amount of borrowing is determined by between-basket similarities. The number of blocks and block memberships are inferred from data based on the posterior probability of each partition. The proposed method is compared to the multisource exchangeability model and Simons two-stage design, respectively. In a variety of simulation scenarios, we demonstrate the proposed method is able to maintain the type I error rate and have desirable basket-wise power. In addition, our method is computationally efficient compared to existing Bayesian methods in that the posterior profiles of interest can be derived explicitly without the need for sampling algorithms.
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Tissue-agnostic trials enroll patients based on their genetic biomarkers, not tumor type, in an attempt to determine if a new drug can successfully treat disease conditions based on biomarkers. The Bayesian hierarchical model (BHM) provides an attractive approach to design phase II tissue-agnostic trials by allowing information borrowing across multiple disease types. In this article, we elucidate two intrinsic and inevitable issues that may limit the use of BHM to tissue-agnostic trials: sensitivity to the prior specification of the shrinkage parameter and the competing interest among disease types in increasing power and controlling type I error. To address these issues, we propose the optimal BHM (OBHM) approach. With OBHM, we first specify a flexible utility function to quantify the tradeoff between type I error and power across disease type based on the study objectives, and then we select the prior of the shrinkage parameter to optimize the utility function of clinical and regulatory interest. OBMH effectively balances type I and II errors, addresses the sensitivity of the prior selection, and reduces the unwarranted subjectivity in the prior selection. Simulation study shows that the resulting OBHM and its extensions, clustered OBHM (COBHM) and adaptive OBHM (AOBHM), have desirable operating characteristics, outperforming some existing methods with better balanced power and type I error control. Our method provides a systematic, rigorous way to apply BHM and solve the common problem of blindingly using a non-informative inverse-gamma prior (with a large variance) or priors arbitrarily chosen that may lead to pathological statistical properties.
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