Bayesian adaptive randomization designs for targeted agent development.

BACKGROUND

With better understanding of the disease's etiology and mechanism, many targeted agents are being developed to tackle the root cause of problems, hoping to offer more effective and less toxic therapies. Targeted agents, however, do not work for everyone. Hence, the development of target agents requires the evaluation of prognostic and predictive markers. In addition, upon the identification of each patient's marker profile, it is desirable to treat patients with best available treatments in the clinical trial accordingly.

METHODS

Many designs have recently been proposed for the development of targeted agents. These include the simple randomization design, marker stratified design, marker strategy design, efficient targeted design, etc. In contrast to the frequentist designs with equal randomization, we propose novel Bayesian adaptive randomization designs that allow evaluating treatments and markers simultaneously, while providing more patients with effective treatments according to the patients' marker profiles. Early stopping rules can be implemented to increase the efficiency of the designs.

RESULTS

Through simulations, the operating characteristics of different designs are compared and contrasted. By carefully choosing the design parameters, types I and II errors can be controlled for Bayesian designs. By incorporating adaptive randomization and early stopping rules, the proposed designs incorporate rational learning from the interim data to make informed decisions. Bayesian design also provides a formal way to incorporate relevant prior information. Compared with previously published designs, the proposed design can be more efficient, more ethical, and is also more flexible in the study conduct.

LIMITATIONS

Response adaptive randomization requires the response to be assessed in a relatively short time period. The infrastructure must be set up to allow timely and more frequent monitoring of interim results.

CONCLUSION

Bayesian adaptive randomization designs are distinctively suitable for the development of multiple targeted agents with multiple biomarkers.