Bayesian adaptive randomization designs for targeted agent development

Abstract

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, type I and type 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 more frequent monitoring of interim results. Conclusion Bayesian adaptive randomization designs are distinctively suitable for the development of multiple targeted agents with multiple biomarkers.

Trial Designs for Personalizing Cancer Care: A Systematic Review and Classification

“…They called it "modified marker-based strategy" design (53). Some other authors referred to the design as "marker-based strategy design II" (45), "augmented strategy" (13), or simply "marker-strategy" (28).…”

“…In cases where a simple 1:1 randomization procedure is applied to all patients, trials are labeled as "simple randomization" (28). However, in cases where the biomarker under evaluation is binary or categorical with few categories, randomization can be done separately for each biomarker category through stratified randomization.…”

Challenges and methodology in the incorporation of biomarkers in cancer clinical trials

“…While equal randomisation can improve the efficiency of a trial by maximizing the statistical power, adaptive randomization offers a higher probability of assigning more patients to a more efficacious treatment, especially when the treatment difference is large or the relevant disease is rare (24). Several types of adaptive randomisation techniques have been proposed, including using short-term response information to facilitate adaptive randomization for survival clinical trials (26), covariateadaptive randomization (24), response-adaptive randomization (24,27) and outcome-based adaptive randomization (21,28); however there may be potential bias if there are any time trends in the prognostic mix of the patients accruing to the trial (28). Patient accrual can be modified using designs such as the adaptive accrual design (16), the biomarker-adaptive parallel Simon two-stage design (29) and the phase III design for the setting of a single binary biomarker stratification design (15) (Table 1).…”

“…Adaptive versions of the aforementioned marker-based designs also have been proposed such as the Bayesian adaptive marker-stratified design (27), the adaptive enrichment design (30,31) and an adaptive version of testing approaches using utility functions (32). Furthermore, a Bayesian prediction model has been proposed to help predict whether a biomarker is truly associated to a clinical outcome using a meta-analytic approach (33).…”

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“…Trial designs for evaluating biomarkers and treatment response have been well described by others, [1][2][3][4][5][6][7] and a brief summary is presented here.…”