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

Zhou, Heng; Murray, Thomas A.; Pan, Haitao; Yuan, Ying

doi:10.1002/sim.7674

Cited by 63 publications

(68 citation statements)

References 28 publications

(43 reference statements)

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“…Model-assisted designs are a relatively new class of designs that combine the superior performance of model-based designs with the simplicity of algorithm-based designs (9,10). Such designs use a model for efficient decision-making like model-based designs, whereas their dose-escalation and de-escalation rules can be tabulated before the onset of a trial, as with algorithmbased designs.…”

Section: Introductionmentioning

confidence: 99%

Accuracy, Safety, and Reliability of Novel Phase I Trial Designs

Zhou

Yuan

Nie

2018

Clinical Cancer Research

Self Cite

108

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A number of novel model-based and model-assisted designs have been proposed to find the MTD in phase I clinical trials, but their differences and relative pros and cons are not clear to many practitioners. We review three model-based designs, including the continual reassessment method (CRM), dose escalation with overdose control (EWOC), and Bayesian logistic regression model (BLRM), and three model-assisted designs, including the modified toxicity probability interval (mTPI), Bayesian optimal interval (BOIN), and keyboard (equivalently mTPI-2) designs. We conduct numerical studies to assess their accuracy, safety, and reliability and the practical implications of various empirical rules used in some designs, such as skipping a dose and imposing overdose control. Our results show that the CRM outperforms EWOC and BLRM with higher accuracy of identifying the MTD. For the CRM, skipping a dose is not recommended, as it substantially increases the chance of overdosing patients while providing limited gain for identifying the MTD. EWOC and BLRM appear excessively conservative. They are safe but have relatively poor accuracy of finding the MTD. The BOIN and keyboard (equivalently mTPI-2) designs have similar operating characteristics, outperforming the mTPI, but the BOIN is more intuitive and transparent. The BOIN yields competitive performance comparable with the CRM but is simpler to implement and free of the issue of irrational dose assignment caused by model misspecification, thereby providing an attractive approach for designing phase I trials. .

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Section: Introductionmentioning

confidence: 99%

Accuracy, Safety, and Reliability of Novel Phase I Trial Designs

Zhou

Yuan

Nie

2018

Clinical Cancer Research

Self Cite

108

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“…The superior performance of adaptive dose‐finding in first‐in‐human oncology dose escalation studies relative to traditional rule‐based (e.g., “3 + 3”) designs is well‐established. Popular model‐assisted designs include the modified toxicity probability interval design and variations, Bayesian optimal interval design, and the Keyboard design, whereas commonly used model‐based designs include continual reassessment method and Bayesian logistic regression model with overdose control 3 . The unique challenges presented by IO drug development present many opportunities to further enhance these well‐developed dose finding designs.…”

Section: Novel Study Designs Well‐suited To Investigational Io Therapiesmentioning

confidence: 99%

Quantitative Translation in Immuno‐Oncology Research and Development

Bottino

Liu

Bazzazi

et al. 2020

Clin Pharma and Therapeutics

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“…To avoid subjectivity in selection of scenarios, we adopt a more objective simulation study based on randomly generated scenarios and compare the proposed interval methods with the model‐based CRM. In particular, 10,000 random dose–toxicity curves, where the location of the individual MTD is uniformly distributed, are generated based on the method described in Zhou et al () for independent and correlated toxicities, respectively. When

Y_{1}

and

Y_{2}

are strictly ordered, an additional constraint with

p_{1 j} \leq p_{2 j}

is imposed,

j = 1, \dots, J

.…”

Section: Numerical Studymentioning

confidence: 99%

“…These model‐assisted interval designs can be implemented in a simple way that is similar to the

3 + 3

design, but yield performance that is comparable to the more complicated model‐based designs, (Oron et al, ; Liu et al, ). Lin and Yuan () and Zhou et al, () showed that, although the model‐assisted designs only utilize the accumulated local data at the current dose for decision making, there is no significant loss of efficiency due to not borrowing information across the doses. 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, ).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimal Interval Design with Multiple Toxicity Constraints

Lin

2018

Biometrics

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

Cited by 63 publications

References 28 publications

Accuracy, Safety, and Reliability of Novel Phase I Trial Designs

Accuracy, Safety, and Reliability of Novel Phase I Trial Designs

Quantitative Translation in Immuno‐Oncology Research and Development

Bayesian Optimal Interval Design with Multiple Toxicity Constraints

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