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Model-Assisted Designs for Early-Phase Clinical Trials: Simplicity Meets…
Model-Assisted Designs for Early-Phase Clinical Trials: Simplicity Meets Superiority
ABSTRACT
introduce and review a class of novel adaptive designs, known as model-assisted designs
Model-assisted designs enjoy superior performance comparable to more complicated, model-based adaptive designs, but their decision rule can be pretabulated and included in the protocol
INTRODUCTION
introduce and review a class of novel phase I and II designs, known as model-assisted designs
Model-assisted designs yield superior performance compared with the conventional algorithm-based designs and are comparable to more complicated (model-based) design
PHASE I TRIAL DESIGNS
algorithm-based： uses a set of simple, prespecified rules to determine the dose escalation and de-escalation
the conventional 3 + 3 design and its extensions： such as the accelerated titration design and the rolling 6 design
poor operating characteristics： for example, it has no specific target dose-limiting toxicity (DLT) rate ，has poor accuracy to identify the MTD, has poor precision to estimate the DLT rate, and has a greater tendency to underdose patients
YET it is simple and easy to implement, the 3 + 3 design is by far the most commonly used phase I design in practice.
model-based： a class of novel adaptive designs that uses a statistical model (eg, a logistic model) to describe the dose-toxicity curve and guide dose transition
the continuous reassessment method (CRM) and its various extensions (eg, dose escalation with overdose control)，Bayesian logistic regression model，Bayesian model averaging CRM
higher accuracy to identify and allocate more patients to the MTD as well as the ability to target any prespecified DLT rates
limited because of statistical and computational complexity of the design
model-assisted designs ： uses a statistical model (eg, the binomial model) to derive the design for efficient decision making; however, like the algorithm-based design, its dose escalation and de-escalation rule can be predetermined before the onset of the trial
the modified toxicity probability interval (mTPI) design and its variation mTPI-2
Bayesian optimal interval (BOIN) design
it outperforms the mTPI with higher accuracy identifying the MTD and a lower risk of overdosing patients, and it is simpler and more transparent than the mTPI-2 and keyboard designs.
more versatile: it can handle drug-combination trials, late-onset toxicity, low-grade toxicities, and toxicity and efficacy jointly
keyboard design