Bayesian Optimal Interval (BOIN) Design Desktop Program
The BOIN design is a novel model-assisted phase I clinical trial design for finding the maximum
tolerated dose (MTD).
It handles single-agent and drug-combination trials, as well as trials with late-onset toxicity.
It strictly adheres to ethical considerations and optimizes the dose assignment for each patient enrolled into
The software (i.e., a Windows desktop program called “BOIN”) implements
the BOIN designs [1,2] for single-agent trials,
single-agent trials with late-onset toxicity ,
drug-combination trials seeking a single MTD 
or the MTD contour .
As a model-assisted design, the BOIN design combines the advantages of algorithm-based designs and the advantages of model-based designs.
It can be implemented in a simple way similar to the traditional algorithm-based 3+3 design, but yields excellent performance comparable to the more complicated model-based designs,
such as the continual reassessment method (CRM) [6, 7].
The BOIN design is motivated by the top priority and concern of clinicians, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses.
- Windows 7 SP1 (but may work on later versions of Windows as well, although this has not been tested)
- Microsoft .NET Framework version 4.6.1 (x86 and x64)
- Windows Installer 4.5
- Minimum screen resolution 1318x860
Note: If you have your display set to use a text size of “Medium - 125%” or “Larger - 150%” you may need a greater screen resolution.
If any required software component is absent from your system, the installation process will install it.
- Self-contained Windows program
- Very easy to use
- Handles single-agent and drug-combination phase I trials
- Accommodates late-onset toxicity/fast accrual to accelerate phase I trials
- Automatically generates a protocol document template
- Enables the user to generate operating characteristics for protocol preparation
- A Trial Conduct tab for conducting drug-combination trials and estimating the single MTD or MTD contour from trial data
Why use the BOIN Design?
Simple to implement Similar to the 3+3 design, the dose
escalation/de-escalation rule is prespecified and can be tabulated. During the trial conduct, clinicians can simply count the
number of patients who experience toxicity and compare the observed toxicity rate with the prespecified
dose escalation/de-escalation boundaries to determine dose assignment until the trial is completed.
For drug-combination trials, the dose escalation/de-escalation rule is the same but the overall conduct of the trial
is more complicated which is why we provide a trial conduct tab for those trials. Nevertheless each decision to escalate the dose,
de-escalate the dose, or remain at the same dose is easy to understand, explain, and predict.
Superior performance Both theoretically and numerically, it has been shown
that the BOIN design has desirable statistical properties and superior operating
characteristics [6 , 7].
Highly Ethical The design optimizes the dose assignment for each patient in
the sense that it minimizes the chance of assigning a patient to either a subtherapeutic dose or an overly
toxic dose. This is consistent with the clinician’s point of view for conducting clinical trials, i.e.,
maximizing the patient’s treatment benefit.
Accommodates both single-agent and drug-combination trials The BOIN design
can be used to design both single-agent and drug-combination phase I trials. The resulting designs are easy
Accommodates late-onset toxicity and fast accrual The time-to-event BOIN (TITE-BOIN) design allows real-time dose assignment decisions for new patients while some enrolled patients’ toxicity data are still pending, thereby significantly accelerating phase I trials.
Solid statistical justifications The design optimizes a sensible
statistical criterion. It has a desirable finite-sample property (i.e., is coherent) and a desirable
large-sample property (i.e., converges to the target dose). See the references for more details.
Extensive context-specific help is provided in the program itself for all aspects of the trial design and how
to use the program. The program produces simulation output which can be incorporated in other documents and
also can automatically generate a protocol document template in either Word or HTML format. An extensive
statistical tutorial, written for the
R package BOIN,
is available separately. This tutorial covers the statistical basis of the method, and has guidelines for how
to use the method.
What’s New in v1.0.7?
A bug was fixed in the TITE-BOIN simulation code.
Richard C. Herrick, Clift Norris, John Venier, and Ying Yuan wrote this program. Ying Yuan, Suyu Liu, Liangcai Zhang, Ruitao Lin, and Heng Zhou originally implemented the numerical algorithms using R.
Single Drug Study:
 Suyu Liu and Ying Yuan (2015)
Bayesian Optimal Interval Designs for Phase I Clinical Trials,
Journal of the Royal Statistical Society: Series C, 64, 507-523.
 Yuan Y., Hess K.R., Hilsenbecck S.G. and Gilbert M.R. (2016)
Bayesian Optimal Interval Design: A Simple and Well-performing Design for Phase I Oncology Trials.
Clinical Cancer Research, 22, 4291-4301.
 Yuan Y., Lin R., Li D., Nie L. and Warren K.E. (2018)
Time-to-event Bayesian Optimal Interval Design to Accelerate Phase I Trials.
Clinical Cancer Research, DOI: 10.1158/1078-0432.CCR-18-0246.
Combination Drug Study Seeking a Single MTD:
 Lin R. and Yin, G. (2017)
Bayesian Optimal Interval Design for Dose Finding in Drug-combination Trials.
Statistical Methods in Medical Research Series, 26, 2155-2167.
Combination Drug Study Seeking an MTD Contour:
 Zhang L. and Y. Yuan. (2016)
A practical Bayesian design to identify the maximum tolerated dose contour for drug combination trials.
Statistics in Medicine, 35, 4924-4936.
Comparison of BOIN to Other Designs:
 Zhou H., Murray T., Pan H. and Yuan Y. (2018)
Comparative review of novel model-assisted designs for phase I clinical trials.,Statistics in Medicine, 37, 2208-2222.
 Zhou H., Yuan Y. and Nie L. (2018)
Accuracy, safety and reliability of novel phase I trial designs.
Clinical Cancer Research, DOI: 10.1158/1078-0432.CCR-18-0168, 2018