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Toxicity probability interval designs have received increasing attention as a dose-finding method in recent years. In this study, we compared the two-stage, likelihood-based continual reassessment method (CRM), modified toxicity probability interval (mTPI), and the Bayesian optimal interval design (BOIN) in order to evaluate each method's performance in dose selection for phase I trials. We use several summary measures to compare the performance of these methods, including percentage of correct selection (PCS) of the true maximum tolerable dose (MTD), allocation of patients to doses at and around the true MTD, and an accuracy index. This index is an efficiency measure that describes the entire distribution of MTD selection and patient allocation by taking into account the distance between the true probability of toxicity at each dose level and the target toxicity rate. The simulation study considered a broad range of toxicity curves and various sample sizes. When considering PCS, we found that CRM outperformed the two competing methods in most scenarios, followed by BOIN, then mTPI. We observed a similar trend when considering the accuracy index for dose allocation, where CRM most often outperformed both mTPI and BOIN. These trends were more pronounced with increasing number of dose levels. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Adaptive, model-based, dose-finding methods, such as the continual reassessment method, have been shown to have good operating characteristics. One school of thought argues in favor of the use of parsimonious models, not modeling all aspects of the problem, and using a strict minimum number of parameters. In particular, for the standard situation of a single homogeneous group, it is common to appeal to a one-parameter model. Other authors argue for a more classical approach that models all aspects of the problem. Here, we show that increasing the dimension of the parameter space, in the context of adaptive dose-finding studies, is usually counter productive and, rather than leading to improvements in operating characteristics, the added dimensionality is likely to result in difficulties. Among these are inconsistency of parameter estimates, lack of coherence in escalation or de-escalation, erratic behavior, getting stuck at the wrong level, and, in almost all cases, poorer performance in terms of correct identification of the targeted dose. Our conclusions are based on both theoretical results and simulations. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]