STEIN: A simple toxicity and efficacy interval design for seamless phase I/II clinical trials

Lin, Ruitao; Yin, Guosheng

doi:10.1002/sim.7428

Cited by 53 publications

(69 citation statements)

References 38 publications

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“…It allows clinicians and regulatory agents to easily assess the safety of the trial design and also allows users to easily calibrate their trials to satisfy the safety requirement mandated by the regulatory agents. In addition, the BOIN design is a versatile method that has been extended to find the MTD or MTD contour for drug combination trials, account for toxicity grades, and conduct phase I‐II trials . Nevertheless, for practitioners who have used and are comfortable with the mTPI design, the Keyboard design provides a useful, seamless upgrade of the mTPI design.…”

Section: Resultsmentioning

confidence: 99%

“…Unlike the model‐based design, such as the CRM, which assumes a dose‐toxicity curve across all doses, the model‐assisted design often models only local data (ie, the data observed at the current dose), typically using a binomial model, which renders it possible to enumerate the dose escalation and de‐escalation rules before the trial begins. Examples of model‐assisted designs include the modified toxicity probability interval (mTPI) design and its variation mTPI‐2, Bayesian optimal interval (BOIN) design, Keyboard design, BOIN combination design and phase I/II design, and Keyboard combination design . Recently, Mu et al proposed a generalized BOIN design that handles toxicity grades, binary or continuous toxicity endpoints under a unified framework.…”

Section: Introductionmentioning

confidence: 99%

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Comparative review of novel model‐assisted designs for phase I clinical trials

Zhou

Murray

Pan

et al. 2018

Statistics in Medicine

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A number of novel phase I trial designs have been proposed that aim to combine the simplicity of algorithm-based designs with the superior performance of model-based designs, including the modified toxicity probability interval, Bayesian optimal interval, and Keyboard designs. In this article, we review these "model-assisted" designs, contrast their statistical foundations and pros and cons, and compare their operating characteristics with the continual reassessment method. To provide unbiased and reliable results, our comparison is based on 10 000 dose-toxicity scenarios randomly generated using the pseudo-uniform algorithm recently proposed in the literature. The results showed that the continual reassessment method, Bayesian optimal interval, and Keyboard designs provide comparable, superior operating characteristics, and each outperforms the modified toxicity probability interval design. These designs are more likely to correctly select the maximum tolerated dose and less likely to overdose patients.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative review of novel model‐assisted designs for phase I clinical trials

Zhou

Murray

Pan

et al. 2018

Statistics in Medicine

View full text Add to dashboard Cite

show abstract

“…where

f false(O_{j}; p_{1 j}, p_{2 j} false)

is the joint binomial likelihood function. Due to the small sample size of the phase I dose‐finding trials, the incorporation of the correlation between the two toxicity outcomes does not necessarily improve the performance of the design (Lin and Yin ). Therefore, we treat the joint likelihood

f false(O_{j}; p_{1 j}, p_{2 j} false)

as the product of independent binomial probability density functions.…”

Section: Boin With Multiple Toxicity Constraintsmentioning

confidence: 99%

“…In addition, studies show that the BOIN and keyboard designs have higher accuracy of identifying the MTD and lower risk of overdosing patients than the mTPI design (Yan et al, ). Model‐assisted designs have been generalized to more complicated early‐phase trials; see Zhang and Yuan () and, Lin and Yin, ().…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimal Interval Design with Multiple Toxicity Constraints

Lin

2018

Biometrics

Self Cite

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Most phase I dose-finding trials are conducted based on a single binary toxicity outcome to investigate the safety of new drugs. In many situations, however, it is important to distinguish between various toxicity types and different toxicity grades. By minimizing the maximum joint probability of incorrect decisions, we extend the Bayesian optimal interval (BOIN) design to control multiple toxicity outcomes at prespecified levels. The developed multiple-toxicity BOIN design can handle equally important, unequally important as well as nested toxicity outcomes. Interestingly, we find that the optimal interval boundaries with non-nested toxicity outcomes under the proposed method coincide with those under the standard single-toxicity BOIN design by treating the multiple toxicity outcomes marginally. We establish several desirable properties for the proposed interval design. We additionally extend our design to address trials with combined drugs. The finite-sample performance of the proposed methods is assessed according to extensive simulation studies and is compared with those of existing methods. Simulation results reveal that, our methods are as accurate and efficient as the more complicated model-based methods, but are more robust and much easier to implement.

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“…The model‐assisted designs are also proposed to identify ODs. Lin and Yin proposed the simple toxicity and efficacy interval (STEIN) design based on optimized intervals for toxicity and efficacy . However, the STEIN design does not consider the accrual rate, the outcome evaluation period, and the late‐onset outcomes.…”

Section: Introductionmentioning

confidence: 99%

TITE‐BOIN‐ET: Time‐to‐event Bayesian optimal interval design to accelerate dose‐finding based on both efficacy and toxicity outcomes

Takeda

Morita

Taguri

2019

Pharmaceutical Statistics

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One of the primary purposes of an oncology dose-finding trial is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent clinical trials. In addition, it is quite important to accelerate early stage trials to shorten the entire period of drug development. However, it is often challenging to make adaptive decisions of dose escalation and de-escalation in a timely manner because of the fast accrual rate, the difference of outcome evaluation periods for efficacy and toxicity and the late-onset outcomes. To solve these issues, we propose the time-to-event Bayesian optimal interval design to accelerate dose-finding based on cumulative and pending data of both efficacy and toxicity. The new design, named "TITE-BOIN-ET" design, is nonparametric and a model-assisted design. Thus, it is robust, much simpler, and easier to implement in actual oncology dosefinding trials compared with the model-based approaches. These characteristics are quite useful from a practical point of view. A simulation study shows that the TITE-BOIN-ET design has advantages compared with the modelbased approaches in both the percentage of correct OD selection and the average number of patients allocated to the ODs across a variety of realistic settings. In addition, the TITE-BOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment and therefore has the potential to accelerate early stage dose-finding trials.

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STEIN: A simple toxicity and efficacy interval design for seamless phase I/II clinical trials

Cited by 53 publications

References 38 publications

Comparative review of novel model‐assisted designs for phase I clinical trials

Comparative review of novel model‐assisted designs for phase I clinical trials

Bayesian Optimal Interval Design with Multiple Toxicity Constraints

TITE‐BOIN‐ET: Time‐to‐event Bayesian optimal interval design to accelerate dose‐finding based on both efficacy and toxicity outcomes

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