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This paper studies the notion of coherence in interval-based dose-finding methods. An incoherent decision is either: (i) a recommendation to escalate the dose following an observed dose-limiting toxicity, or (ii) a recommendation to de-escalate the dose following a non-dose-limiting toxicity. In a simulated example, we illustrate that the Bayesian optimal interval method and the Keyboard method are not coherent. We generated dose-limiting toxicity outcomes under an assumed set of true probabilities for a trial of n = 36 patients in cohorts of size 1, and we counted the number of incoherent dosing decisions that were made throughout this simulated trial. Each of the methods studied resulted in 13/36 (36%) incoherent decisions in the simulated trial. Additionally, for two different target dose-limiting toxicity rates, 20% and 30%, and a sample size of n = 30 patients, we randomly generated 100 dose-toxicity curves and tabulated the number of incoherent decisions made by each method in 1000 simulated trials under each curve. For each method studied, the probability of incurring at least one incoherent decision during the conduct of a single trial is greater than 75%. Coherency is an important principle in the conduct of dose-finding trials. Interval-based methods violate this principle for cohorts of size 1 and require additional modifications to overcome this shortcoming. Researchers need take a closer look at the dose assignment behavior of interval-based methods when using them to plan dose-finding studies.

We propose a new design for dose finding for cytotoxic agents in two ordered groups of patients. By ordered groups, we mean that prior to the study there is clinical information that would indicate that for a given dose one group would be more susceptible to toxicities than patients in the other group. The designs are evaluated relative to two previously proposed designs for ordered groups over a range of scenarios generated randomly from a family of dose-toxicity curves. Copyright © 2016 John Wiley & Sons, Ltd.

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.

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.

There has been constant development of novel statistical methods in the design of early-phase clinical trials since the introduction of model-based designs, yet the traditional or modified 3+3 algorithmic design remains the most widely used approach in dose-finding studies. Research has shown the limitations of this traditional design compared with more innovative approaches yet the use of these model-based designs remains infrequent. This can be attributed to several causes including a poor understanding from clinicians and reviewers into how the designs work, and how best to evaluate the appropriateness of a proposed design. These barriers are likely to be enhanced in the coming years as the recent paradigm of drug development involves a shift to more complex dose-finding problems. This article reviews relevant information that should be included in clinical trial protocols to aid in the acceptance and approval of novel methods. We provide practical guidance for implementing these efficient designs with the aim of augmenting a broader transition from algorithmic to adaptive model-guided designs. In addition we highlight issues to consider in the actual implementation of a trial once approval is obtained. Copyright © 2016 John Wiley & Sons, Ltd.

The majority of phase I methods for multi-agent trials have focused on identifying a single maximum tolerated dose combination (MTDC) among those being investigated. Some published methods in the area have been based on the notion that there is no unique MTDC and that the set of dose combinations with acceptable toxicity forms an equivalence contour in two dimensions. Therefore, it may be of interest to find multiple MTDCs for further testing for efficacy in a phase II setting. In this paper, we present a new dose-finding method that extends the continual reassessment method to account for the location of multiple MTDCs. Operating characteristics are demonstrated through simulation studies and are compared with existing methodology. Some brief discussion of implementation and available software is also provided. Copyright © 2016 John Wiley & Sons, Ltd.

Dose-finding studies that aim to evaluate the safety of single agents are becoming less common, and advances in clinical research have complicated the paradigm of dose finding in oncology. A class of more complex problems, such as targeted agents, combination therapies and stratification of patients by clinical or genetic characteristics, has created the need to adapt early-phase trial design to the specific type of drug being investigated and the corresponding endpoints. In this article, we describe the implementation of an adaptive design based on a continual reassessment method for heterogeneous groups, modified to coincide with the objectives of a phase I/II trial of stereotactic body radiation therapy (SBRT) in patients with painful osseous metastatic disease. Operating characteristics of the Institutional Review Board (IRB) approved design are demonstrated under various possible true scenarios via simulation studies.

In a recent paper, Riviere, Dubois, and Zohar [1] conducted a comparative study of dose-finding methods for two-agent combination phase I oncology trials. One of the methods presented by the authors in their comparison was the partial order continual reassessment method (POCRM; [2]). Unfortunately, some of the design choices made by the authors in their implementation of POCRM hindered its performance in some scenarios. The purpose of this letter is to show how Riviere et al. [1] could have better implemented POCRM in conducting their simulation study. Specifically, the following claims are addressed and further clarification provided to ensure that accurate implementation of POCRM is followed. “As most real clinical trials begin at the lowest dose, we think that this method (POCRM) should be modified to allow initiating the trial at the lowest combination while adding ‘nonskipping’ rules or a start-up phase.” POCRM has already been modified to include a start-up phase that begins at the lowest dose combination, and work that was published by Wages, Conaway and O’Quigley [3] in 2011 evaluated the operating characteristics of this modification. POCRM was the only model-based method of those compared by Riviere et al. [1] in which the authors chose not to include a start-up phase, even though a modified version of POCRM that includes such a phase has already been published [3], and is accompanied by publically available software [4]. “Another important modification that we have added to the POCRM is the recommendation to start at the lowest dose. We have based our decision on common practice in Phase I for a single agent or a combination of agents.” As stated above, POCRM has already been modified to begin at the lowest dose combination [3]. In contrast, the modification made to POCRM by Riviere et al. [1] was not only to begin at the lowest dose of each agent (i.e. combination D1,1), but to do so within a Bayesian framework using priors and skeletons that center the prior MTD at combinations D3,3, D4,3 or D5,2, depending on the dose-toxicity ordering. After beginning the trial at D1,1, in the absence of DLT in the first cohort, this modification will cause the design to gravitate toward the prior MTD (i.e. D3,3, D4,3 or D5,2), and such behavior would not be allowed in practice, as pointed out by the authors. Therefore, the modification made by the authors does not resolve any practical issue associated with the POCRM. The authors have chosen to make a modification that is not allowable in practice within an article that is meant to inform statisticians about how best to practically conduct Phase I trials of combinations. Wages and Conaway [5] published practical guidelines for implementing POCRM, yet the comparison by Riviere et al. [1] did not follow these recommendations, especially in choosing an appropriate subset of possible dose-toxicity orderings. The guidelines in [5], combined with an R package (pocrm [4]), have led to the successful implementation of POCRM in two ongoing trials since its publication in 2011. One recently completed accrual and is in follow-up (Mel 58) and the other (Mel 60) is open to accrual at two sites, the University of Virginia Cancer Center and MD Anderson Cancer Center. Descriptions of these trials can be found at https://clinicaltrials.gov/ct2/show/{"type":"clinical-trial","attrs":{"text":"NCT01585350","term_id":"NCT01585350"}}NCT01585350?term=mel58&rank=1 and https://clinicaltrials.gov/ct2/show/{"type":"clinical-trial","attrs":{"text":"NCT02126579","term_id":"NCT02126579"}}NCT02126579?term=mel60&rank=1. The use of appropriate POCRM specifications in the comparative work of Riviere et al. [1] would have resulted in a more accurate evaluation of POCRM’s design characteristics, and provided the readers of Statistics in Medicine valuable insight on how the method fares relative to existing methods. In this letter, we focus our attention on proper selection of possible dose-toxicity orderings for use in the POCRM. In the subsequent sections, we review the recommendations of Wages and Conaway [5] in choosing orderings, and discuss their impact on the operating characteristics of POCRM. This sheds a very different light on the results and conclusions of Riviere [1].

Existing statistical methodology on dose finding for combination chemotherapies has focused on toxicity considerations alone in finding a maximum tolerated dose combination to recommend for further testing of efficacy in a phase II setting. Recently, there has been increasing interest in integrating phase I and phase II trials in order to facilitate drug development. In this article, we propose a new adaptive phase I/II method for dual-agent combinations that takes into account both toxicity and efficacy after each cohort inclusion. The primary objective, both within and at the conclusion of the trial, becomes finding a single dose combination with an acceptable level of toxicity that maximizes efficacious response. We assume that there exist monotone dose–toxicity and dose–efficacy relationships among doses of one agent when the dose of other agent is fixed. We perform extensive simulation studies that demonstrate the operating characteristics of our proposed approach, and we compare simulated results to existing methodology in phase I/II design for combinations of agents.

The majority of methods for the design of Phase I trials in oncology are based upon a single course of therapy, yet in actual practice it may be the case that there is more than one treatment schedule for any given dose. Therefore, the probability of observing a dose-limiting toxicity (DLT) may depend upon both the total amount of the dose given, as well as the frequency with which it is administered. The objective of the study then becomes to find an acceptable combination of both dose and schedule. Past literature on designing these trials has entailed the assumption that toxicity increases monotonically with both dose and schedule. In this article, we relax this assumption for schedules and present a dose-schedule finding design that can be generalized to situations in which we know the ordering between all schedules and those in which we do not. We present simulation results that compare our method to other suggested dose-schedule finding methodology.

In studies of combinations of agents in phase I oncology trials, the dose–toxicity relationship may not be monotone for all combinations, in which case the toxicity probabilities follow a partial order. The continual reassessment method for partial orders (PO-CRM) is a design for phase I trials of combinations that leans upon identifying possible complete orders associated with the partial order. This article addresses some practical design considerations not previously undertaken when describing the PO-CRM. We describe an approach in choosing a proper subset of possible orderings, formulated according to the known toxicity relationships within a matrix of combination therapies. Other design issues, such as working model selection and stopping rules, are also discussed. We demonstrate the practical ability of PO-CRM as a phase I design for combinations through its use in a recent trial designed at the University of Virginia Cancer Center. Copyright © 2013 John Wiley & Sons, Ltd.

The time-to-event continual reassessment method (TITE-CRM) was proposed to handle the problem of long trial duration in Phase 1 trials as a result of late-onset toxicities. Here, we implement the TITE-CRM in dose–finding trials of combinations of agents. When studying multiple agents, monotonicity of the dose-toxicity curve is not clearly defined. Therefore, the toxicity probabilities follow a partial order, meaning that there are pairs of treatments for which the ordering of the toxicity probabilities is not known at the start of the trial. A CRM design for partially ordered trials (PO-CRM) was recently proposed. Simulation studies show that extending the TITE-CRM to the partial order setting produces results similar to those of the PO-CRM in terms of maximum tolerated dose recommendation yet reduces the duration of the trial.

In most cancer dose-finding studies, the toxicities are graded typically from 0 (the absence of any side effects) to 4 based on the presence of serious and potentially life-threatening toxicity. Usually, these grades are subsequently recoded whereby all toxicity grades between 0 and 3 are given the value 0 and are considered to be non-dose-limiting toxicities (DLT), and grade 4 (a dose-limiting toxicity) is coded as 1. We describe here how greater use can be made of this information, which potentially leads to more efficient and effective dose-finding studies. Our focus is on two-stage continual reassessment method (CRM) designs. During this design, an initial escalation stage requires no use of a model until the first toxicity is encountered, at which time the second stage, based on a model, kicks in. In this context, two possible ways can be used to make use of information on toxicity grades. We can make use of this information in either one of these two stages in different ways. In the first stage, the ideas are usually straightforward that appeal mostly to common-sense arguments. Nonetheless, it can be observed that use of graded toxicity information in the first stage alone can make an important contribution to increased efficiency. Use of graded toxicity information in the second stage requires an additional model to demonstrate the rate of toxicities. This method is described as well as the potential improvements in performance, which can be achieved by the use of such models. Keywords: clinical trial; continual reassessment method; dose escalation; dose finding studies; escalation; grades; phase 1 trial; toxicity; two-stage designs