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Performance of toxicity probability interval based designs in contrast to the continual reassessment method.

ABSTRACT: 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.

SUBMITTER: Horton BJ

PROVIDER: S-EPMC5267938 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Performance of toxicity probability interval based designs in contrast to the continual reassessment method.

Horton Bethany Jablonski BJ Wages Nolan A NA Conaway Mark R MR

Statistics in medicine 20160719 2

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 ...[more]

PMID: 27435150

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Project description:BackgroundThis article studies the notion of irrational dose assignment in Phase I clinical trials. This property was recently defined by Zhou and colleagues as a dose assignment that fails to de-escalate the dose when two out of three, three out of six, or four out of six patients have experienced a dose-limiting toxicity event at the current dose level. The authors claimed that a drawback of the well-known continual reassessment method is that it can result in irrational dose assignments. The aim of this article is to examine this definition of irrationality more closely within the conduct of the continual reassessment method.MethodsOver a broad range of assumed dose-limiting toxicity probability scenarios for six study dose levels and a variety of target dose-limiting toxicity rates, we simulated 2000 trials of n = 36 patients. For each scenario, we counted the number of irrational dose assignments that were made by the continual reassessment method, according to the definitions of Zhou and colleagues. For each of the irrational decisions made, we classified the dose assignment as an underdose assignment, a target dose assignment, or an overdose assignment based on the true dose-limiting toxicity probability at that dose.ResultsAcross eight dose-toxicity scenarios, there were a total of 181,581 dose assignments made in the simulation study. Of these assignments, 8165 (4.5%) decisions were made when two out of three, three out of six, or four out of six patients had experienced a dose-limiting toxicity at the current dose. Of these 8165 decisions, 1505 (18.4%) recommended staying at the current dose level and would therefore be classified as irrational by Zhou and colleagues. Among the irrational decisions, 41.2% were misclassified, meaning they were made either at the true target dose (17.9%) or at a true underdose (23.3%). The remaining 58.8% were made at a true overdose and therefore truly irrational. Overall, irrational dose assignments comprised <1% of the total dose assignments made during the simulation study. Similar findings are reported in simulations across 100 randomly generated dose-toxicity scenarios from a recently proposed family of curves.ConclusionZhou and colleagues argue that the behavior of the continual reassessment method is disturbing due to its ability to make irrational dose assignments. These definitions are based on rules that mimic the popular 3 + 3 design, which should not be the benchmark used to construct guidelines for trial conduct of modern Phase I methods. Our study illustrates that these dose assignments occur very seldom in the continual reassessment method and that even when they do occur, they can often be considered sensible when accounting for all accumulated data in the study.

Project description:IntroductionThe continual reassessment method (CRM) is a model-based design for phase I trials, which aims to find the maximum tolerated dose (MTD) of a new therapy. The CRM has been shown to be more accurate in targeting the MTD than traditional rule-based approaches such as the 3 + 3 design, which is used in most phase I trials. Furthermore, the CRM has been shown to assign more trial participants at or close to the MTD than the 3 + 3 design. However, the CRM's uptake in clinical research has been incredibly slow, putting trial participants, drug development and patients at risk. Barriers to increasing the use of the CRM have been identified, most notably a lack of knowledge amongst clinicians and statisticians on how to apply new designs in practice. No recent tutorial, guidelines, or recommendations for clinicians on conducting dose-finding studies using the CRM are available. Furthermore, practical resources to support clinicians considering the CRM for their trials are scarce.MethodsTo help overcome these barriers, we present a structured framework for designing a dose-finding study using the CRM. We give recommendations for key design parameters and advise on conducting pre-trial simulation work to tailor the design to a specific trial. We provide practical tools to support clinicians and statisticians, including software recommendations, and template text and tables that can be edited and inserted into a trial protocol. We also give guidance on how to conduct and report dose-finding studies using the CRM.ResultsAn initial set of design recommendations are provided to kick-start the design process. To complement these and the additional resources, we describe two published dose-finding trials that used the CRM. We discuss their designs, how they were conducted and analysed, and compare them to what would have happened under a 3 + 3 design.ConclusionsThe framework and resources we provide are aimed at clinicians and statisticians new to the CRM design. Provision of key resources in this contemporary guidance paper will hopefully improve the uptake of the CRM in phase I dose-finding trials.

Project description:BACKGROUND:A key challenge in phase I trials is maintaining rapid escalation in order to avoid exposing too many patients to sub-therapeutic doses, while preserving safety by limiting the frequency of toxic events. Traditional rule-based designs require temporarily stopping recruitment whilst waiting to see whether enrolled patients develop toxicity. This can be both inefficient and introduces logistic challenges to recruitment in the clinic. We describe a novel two-stage dose assignment procedure designed for a phase I clinical trial (STARPAC), where a good estimation of prior was possible. METHODS:The STARPAC design uses rule-based design until the first patient has a dose limiting toxicity (DLT) and then switches to a modified CRM, with rules to handle patient recruitment during follow-up of earlier patients. STARPAC design is compared via simulations with the TITE-CRM and 3?+?3 methods in various toxicity estimate (T1-5), rate of recruitment (R1-2), and DLT events timing (DT1-4), scenarios using several metrics: accuracy of maximum tolerated dose (MTD), numbers of DLTs, number of patients enrolled and those missed; duration of trial; and proportion of patients treated at the therapeutic dose or MTD. RESULTS:The simulations suggest that STARPAC design performed well in MTD estimation and in treating patients at the highest possible therapeutic levels. STARPAC and TITE-CRM were comparable in the number of patients required and DLTs incurred. The 3?+?3 design often had fewer patients and DLTs although this is due to its low escalation rate leading to poor MTD estimation. For the numbers of declined patients and MTD estimation 3?+?3 is uniformly worse, with STARPAC being better in those metrics for high toxicity scenarios and TITE-CRM better with low toxicity. In situations including doses with toxicities both above and below 30%, the STARPAC design outperformed TITE-CRM with respect to every metric. CONCLUSION:When considering doses with toxicities both above and below the target of 30% toxicities, the two-stage STARPAC dose escalation design provides a more efficient phase I trial design than either the traditional 3?+?3 or the TITE-CRM design. Trialists should model various designs via simulation to adopt the most efficient design for their clinical scenario. TRIAL REGISTRATION:Clinical Trials NCT03307148 (11 October 2017).

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Performance of toxicity probability interval based designs in contrast to the continual reassessment method.

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Performance of toxicity probability interval based designs in contrast to the continual reassessment method.

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