Nolan A Wages1, Mark R Conaway, John O'Quigley. 1. aDivision of Translational Research and Applied Statistics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
Abstract
BACKGROUND: The two-stage, likelihood-based continual reassessment method (CRM-L) entails the specification of a set of design parameters prior to the beginning of its use in a study. The impression of clinicians is that the success of model-based designs, such as CRM-L, depends upon some of the choices made with regard to these specifications, such as the choice of parametric dose-toxicity model and the initial guess of toxicity probabilities. PURPOSE: In studying the efficiency and comparative performance of competing dose-finding designs for finite (typically small) samples, the nonparametric optimal benchmark is a useful tool. When comparing a dose-finding design to the optimal design, we are able to assess how much room there is for potential improvement. METHODS: The optimal method, based only on an assumption of monotonicity of the dose-toxicity function, is a valuable theoretical construct serving as a benchmark in theoretical studies, similar to that of a Cramér-Rao bound. We consider the performance of CRM-L under various design specifications and how it compares to the optimal design across a range of practical situations. RESULTS: Using simple recommendations for design specifications, the CRM-L will produce performances, in terms of identifying doses at and around the maximum tolerated dose (MTD), that are close to the optimal method on average over a broad group of dose-toxicity scenarios. LIMITATIONS: Although the simulation settings vary in the number of doses considered, the target toxicity rate, and the sample size, the results here are presented for a small, though widely used, set of two-stage CRM designs. CONCLUSIONS: Based on simulations here, and many others not shown, CRM-L is almost as accurate, in many scenarios, as the nonparametric optimal design. On average, there appears to be very little margin for improvement. Even if a finely tuned skeleton offers some improvement over a simple skeleton, the improvement is necessarily very small.
BACKGROUND: The two-stage, likelihood-based continual reassessment method (CRM-L) entails the specification of a set of design parameters prior to the beginning of its use in a study. The impression of clinicians is that the success of model-based designs, such as CRM-L, depends upon some of the choices made with regard to these specifications, such as the choice of parametric dose-toxicity model and the initial guess of toxicity probabilities. PURPOSE: In studying the efficiency and comparative performance of competing dose-finding designs for finite (typically small) samples, the nonparametric optimal benchmark is a useful tool. When comparing a dose-finding design to the optimal design, we are able to assess how much room there is for potential improvement. METHODS: The optimal method, based only on an assumption of monotonicity of the dose-toxicity function, is a valuable theoretical construct serving as a benchmark in theoretical studies, similar to that of a Cramér-Rao bound. We consider the performance of CRM-L under various design specifications and how it compares to the optimal design across a range of practical situations. RESULTS: Using simple recommendations for design specifications, the CRM-L will produce performances, in terms of identifying doses at and around the maximum tolerated dose (MTD), that are close to the optimal method on average over a broad group of dose-toxicity scenarios. LIMITATIONS: Although the simulation settings vary in the number of doses considered, the target toxicity rate, and the sample size, the results here are presented for a small, though widely used, set of two-stage CRM designs. CONCLUSIONS: Based on simulations here, and many others not shown, CRM-L is almost as accurate, in many scenarios, as the nonparametric optimal design. On average, there appears to be very little margin for improvement. Even if a finely tuned skeleton offers some improvement over a simple skeleton, the improvement is necessarily very small.
Authors: Anuradha Banerjee; Regina I Jakacki; Arzu Onar-Thomas; Shengjie Wu; Theodore Nicolaides; Tina Young Poussaint; Jason Fangusaro; Joanna Phillips; Arie Perry; David Turner; Michael Prados; Roger J Packer; Ibrahim Qaddoumi; Sridharan Gururangan; Ian F Pollack; Stewart Goldman; Lawrence A Doyle; Clinton F Stewart; James M Boyett; Larry E Kun; Maryam Fouladi Journal: Neuro Oncol Date: 2017-08-01 Impact factor: 12.300