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Bayesian Optimal Interval Design: A Simple and Well-Performing Design for Phase I Oncology Trials.

ABSTRACT: Despite more than two decades of publications that offer more innovative model-based designs, the classical 3 + 3 design remains the most dominant phase I trial design in practice. In this article, we introduce a new trial design, the Bayesian optimal interval (BOIN) design. The BOIN design is easy to implement in a way similar to the 3 + 3 design, but is more flexible for choosing the target toxicity rate and cohort size and yields a substantially better performance that is comparable with that of more complex model-based designs. The BOIN design contains the 3 + 3 design and the accelerated titration design as special cases, thus linking it to established phase I approaches. A numerical study shows that the BOIN design generally outperforms the 3 + 3 design and the modified toxicity probability interval (mTPI) design. The BOIN design is more likely than the 3 + 3 design to correctly select the MTD and allocate more patients to the MTD. Compared with the mTPI design, the BOIN design has a substantially lower risk of overdosing patients and generally a higher probability of correctly selecting the MTD. User-friendly software is freely available to facilitate the application of the BOIN design. Clin Cancer Res; 22(17); 4291-301. ©2016 AACR.

SUBMITTER: Yuan Y

PROVIDER: S-EPMC5047439 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Bayesian Optimal Interval Design: A Simple and Well-Performing Design for Phase I Oncology Trials.

Yuan Ying Y Hess Kenneth R KR Hilsenbeck Susan G SG Gilbert Mark R MR

Clinical cancer research : an official journal of the American Association for Cancer Research 20160712 17

Despite more than two decades of publications that offer more innovative model-based designs, the classical 3 + 3 design remains the most dominant phase I trial design in practice. In this article, we introduce a new trial design, the Bayesian optimal interval (BOIN) design. The BOIN design is easy to implement in a way similar to the 3 + 3 design, but is more flexible for choosing the target toxicity rate and cohort size and yields a substantially better performance that is comparable with that ...[more]

PMID: 27407096

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Project description:Objective: With the increasing recognition of the over-diagnosis and over-treatment of prostate cancer (PCa), the choice of a better prostate biopsy strategy had confused both the patients and clinical surgeons. Hence, this network meta-analysis was conducted to clarify this question. Methods: In the current network meta-analysis, twenty eligible randomized controlled trials (RCTs) with 4,571 participants were comprehensively identified through Pubmed, Embase and Web of Science databases up to July 2017. The pooled odds ratio (OR) with 95% credible interval (CrI) was calculated by Markov chain Monte Carlo methods. A Bayesian network meta-analysis was conducted by using R-3.4.0 software with the help of package "gemtc" version 0.8.2. Results: Six different PCa biopsy strategies and four clinical outcomes were ultimately analyzed in this study. Although, the efficacy of different PCa biopsy strategies by ORs with corresponding 95% CrIs had not yet reached statistical differences, the cumulative rank probability indicated that overall PCa detection rate from best to worst was FUS-GB plus TRUS-GB, FUS-GB, CEUS, MRI-GB, TRUS-GB and TPUS-GB. In terms of clinically significant PCa detection, CEUS, FUS-GB or FUS-GB plus TRUS-GB had a higher, whereas TRUS-GB or TPUS-GB had a relatively lower significant detection rate. Meanwhile, TPUS-GB or TRUS-GB had a higher insignificant PCa detection rate. As for TRUS-guided biopsy, the general trend was that the more biopsy cores, the higher overall PCa detection rate. As for targeted biopsy, it could yield a comparable or even a better effect with fewer cores, compared with traditional random biopsy. Conclusion: Taken together, in a comprehensive consideration of four clinical outcomes, our outcomes shed light on that FUS-GB or FUS-GB plus TRUS-GB showed their superiority, compared with other puncture methods in the detection of PCa. Moreover, TPUS or TRUS-GB was more easily associated with the over-diagnosis and over-treatment of PCa. In addition, targeted biopsy was obviously more effective than traditional random biopsy. The subsequent RCTs with larger sample sizes were required to validate our findings.

Project description:BackgroundMulti-Arm Multi-Stage designs aim at comparing several new treatments to a common reference, in order to select or drop any treatment arm to move forward when such evidence already exists based on interim analyses. We redesigned a Bayesian adaptive design initially proposed for dose-finding, focusing our interest in the comparison of multiple experimental drugs to a control on a binary criterion measure.MethodsWe redesigned a phase II clinical trial that randomly allocates patients across three (one control and two experimental) treatment arms to assess dropping decision rules. We were interested in dropping any arm due to futility, either based on historical control rate (first rule) or comparison across arms (second rule), and in stopping experimental arm due to its ability to reach a sufficient response rate (third rule), using the difference of response probabilities in Bayes binomial trials between the treated and control as a measure of treatment benefit. Simulations were then conducted to investigate the decision operating characteristics under a variety of plausible scenarios, as a function of the decision thresholds.ResultsOur findings suggest that one experimental treatment was less efficient than the control and could have been dropped from the trial based on a sample of approximately 20 instead of 40 patients. In the simulation study, stopping decisions were reached sooner for the first rule than for the second rule, with close mean estimates of response rates and small bias. According to the decision threshold, the mean sample size to detect the required 0.15 absolute benefit ranged from 63 to 70 (rule 3) with false negative rates of less than 2 % (rule 1) up to 6 % (rule 2). In contrast, detecting a 0.15 inferiority in response rates required a sample size ranging on average from 23 to 35 (rules 1 and 2, respectively) with a false positive rate ranging from 3.6 to 0.6 % (rule 3).ConclusionAdaptive trial design is a good way to improve clinical trials. It allows removing ineffective drugs and reducing the trial sample size, while maintaining unbiased estimates. Decision thresholds can be set according to predefined fixed error decision rates.Trial registrationClinicalTrials.gov Identifier: NCT01342692 .

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Bayesian Optimal Interval Design: A Simple and Well-Performing Design for Phase I Oncology Trials.

Publications

Bayesian Optimal Interval Design: A Simple and Well-Performing Design for Phase I Oncology Trials.

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