Comparative Study of Bayesian Information Borrowing Methods in Oncology Clinical Trials

Abstract

Purpose: With deeper insight into precision medicine, more innovative oncology trial designs have been proposed to contribute to the characteristics of novel antitumor drugs. Bayesian information borrowing is an indispensable part of these designs, which shows great advantages in improving the efficiency of clinical trials. Bayesian methods provide an effective framework when incorporating information. However, the key point lies in how to choose an appropriate method for complex oncology clinical trials.

Methods: We divided the borrowing information scenarios into concurrent and nonconcurrent scenarios according to whether the data to be borrowed are observed at the same time as in the current trial or not. Then, we provided an overview of the methods in each scenario. Performance comparison of different methods is carried out with regard to the type I error and power.

Results: As demonstrated by the simulation results in each borrowing scenario, the Bayesian hierarchical model and its extensions are more appropriate for concurrent borrowing. The simulation results demonstrate that the Bayesian hierarchical model shows great advantages when the arms are homogeneous. However, such a method should be adopted with caution when heterogeneity exists. We recommend the other methods, considering heterogeneity. Borrow information from informative priors is more suggested for nonconcurrent borrowing scenarios. Multisource exchangeability models are more suitable for multiple historical trials, while meta-analytic-predictive prior should be carefully applied.

Conclusion: Bayesian information borrowing is useful and can improve the efficiency of clinical trial designs. However, we should carefully choose an appropriate information borrowing method when facing a practical innovative oncology trial, as an appropriate method is essential to provide ideal design performance.

FIG 1.

Simulation results for scenarios of a single historical trial. (A) Type I error under the null hypothesis against different ORR in current control arm. (B) Bias under the null hypothesis against different ORR in current control arm. (C) Power under the alternative hypothesis against different ORR in current control arm. (D) Bias under the alternative hypothesis against different ORR in current control arm. CP, commensurate prior; CPP, calibrated power prior; MAP, meta-analytic-predictive prior; MEMs, multisource exchangeability models; MPP, modified power prior; ORR, objective response rate; PP, power prior; PvPP, P value–based power prior.

FIG 2.

Decision-making diagram for how to choose an appropriate borrowing information method. BaCIS, Bayesian hierarchical classification and information sharing; BCHM, Bayesian cluster hierarchical model; BHM, Bayesian hierarchical model; CBHM, calibrated Bayesian hierarchical model; CP, commensurate prior; CPP, calibrated power prior; MAP, meta-analytic-predictive prior; MEM, multisource exchangeability model; MPP, modified power prior; PP, power prior; PvPP, P value–based power prior; RMAP, robust meta-analytic-predictive prior.