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This paper deals with the design of the likelihood continual reassessment method, which is an increasingly widely used model-based method for dose-finding studies. It is common to implement the method in a two-stage approach, whereby the model-based stage is activated after an initial sequence of patients has been treated. While this two-stage approach is practically appealing, it lacks a theoretical framework, and it is often unclear how the design components should be specified. This paper develops a general framework based on the coherence principle, from which we derive a design calibration process. A real clinical-trial example is used to demonstrate that the proposed process can be implemented in a timely and reproducible manner, while offering competitive operating characteristics. We explore the operating characteristics of different models within this framework and show the performance to be insensitive to the choice of dose-toxicity model.

The idea of bridging in dose-finding studies is closely linked to the problem of group heterogeneity. There are some distinctive features in the case of bridging which need to be considered if efficient estimation of the maximum tolerated dose (MTD) is to be accomplished. The case of two distinct populations is considered. In the bridging setting we usually have in mind two studies, corresponding to the two populations. In some cases, the first of these studies may have been completed while the second has yet to be initiated. In other cases, the studies take place simultaneously and information can then be shared among the two groups. The methodological problem is how to make most use of the information gained in the first study to help improve efficiency in the second. We describe the models that we can use for the purpose of bridging and study situations in which their use leads to overall improvements in performance as well as cases where there is no gain when compared to carrying out parallel studies. Simulations and an example in pediatric oncology help to provide further insight.

Many dose-finding approaches that could evaluate bivariate binary efficacy and toxicity outcomes have been proposed in recent years. In such designs, the operating characteristics with finite sample size can be greatly affected by the assumed dose-toxicity and/or dose-efficacy relationship. However, we do not have much information about a new agent we investigated at the planning stage of Phase I trials and so always face to the risk of misspecifying the true dose-toxicity and/or dose-efficacy relationship by arbitrarily and subjectively choosing skeletons. In this article, we proposed the Bayesian model averaging bivariate continual reassessment method to cope with above risk.

In phase I cancer studies, the maximum tolerated dose (MTD) is estimated by gradually increasing dose levels while accumulating safety information. Recently, Bayesian dose-finding methods such as the continual reassessment method (CRM) have gained popularity. Due to the lack of safety information, phase I studies on new drugs must start at doses low enough that efficacy is not expected but safety is certain up to an acceptable level. To reach the MTD with fewer patients, a 2-stage method has been proposed that enrolls only a single patient at each dose level until the first dose-limiting toxicity is observed. If the study drug is less toxic, it may require many cohorts to complete the study and thus may lead to a longer study period. In this paper, the authors propose a new CRM with cohort size adaptation to reduce the number of cohorts without reducing the accuracy of MTD selection. The cohort size is determined based on the Bayesian posterior probabilities computed during a study. Simulation studies show that the proposed method reduced the number of cohorts compared with the 2-stage method while still yielding a comparable probability of selecting the MTD correctly.

Phase I drug-combination trials are becoming commonplace in oncology. Most of the current dose-finding designs aim to quantify the toxicity probability space using certain prespecified yet complicated models. These models need to characterize not only each individual drug’s toxicity profile, but also their interaction effects, which often leads to multi-parameter models. We propose a novel Bayesian adaptive design for drug-combination trials based on a robust dimension-reduction method. We continuously update the order of dose combinations and reduce the two-dimensional searching space to a one-dimensional line based on the estimated order. As a result, the common approaches to single-agent dose finding, such as the continual reassessment method (CRM), can be applied to drug-combination trials. We further utilize the ensemble technique in machine learning, the so-called bootstrap aggregating (bagging) in conjunction with Bayesian model averaging, to enhance the efficiency and reduce the variability of the proposed method. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various scenarios. Compared with existing competitive designs, the bagging CRM demonstrates its precision and robustness in terms of pinning down the correct dose combination. We apply the proposed bagging CRM to two recent cancer clinical trials with combined drugs for dose finding.

Ever escalating costs of pharmaceutical research and development [1] have prompted a drive for greater efficiency in clinical trial design. This has included use of surrogate outcomes [2], biomarker/genetic-based designs [3] and promotion of a range of adaptive designs [4]. While the broader class of adaptive designs includes adaptive randomization (using either minimization or stratification), here we focus on response adaptive designs, where the design is modified during the course of the trial based on the responses (either on a safety indicator or an efficacy outcome or both) of participants who have already been enrolled in the trial. Here, we undertake a brief tour of three key areas in which adaptive designs provide an effective contribution to drug development and then consider some implications of applying adaptive designs in practice.

China is new to Bayesian adaptive designs. In Phase I, all designs are adaptive and Bayesian designs have been heavily re- searched and implemented in US. However, in China, especially inland areas, there ordinarily lacks logistical foundations and sufficient num- bers of qualified biostatisticans to equip clinical trials with advanced designs. This paper focuses on the phase I oncology studies and explores the performance, practical issues, and potential usability of two Bayesian designs: the continual reassessment method (CRM ) and the modi- fied toxic probability interval (mTPI) design. We conclude that both designs provide desirable operating characteristics but the mTPI design is more suitable for China due to its transparency and simplicity. For example, mTPI does not require real-time online trial conduct tools that are otherwise needed to implement CRM. In addition, we propose a minor modification of the original mTPI that results in a sample size reduction compared to the original mTPI method.

This paper presents the R package pocrm for implementing and simulating the partial order continual reassessment method (PO-CRM; [1,2]) in Phase I trials of combinations of agents. The aim of this article is to illustrate, through examples of the pocrm package, how the PO-CRM works and how its operating characteristics can inform clinical trial investigators. This should promote the use of the PO-CRM in designing and conducting dose-finding Phase I trials of combinations of agents.

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 Previously reported estimates of the ED95 doses for local anesthetics used in brachial plexus blocks vary. The authors used the continual reassessment method, already established in oncology trials, to determine the ED95 dose for 0.5% bupivacaine for the ultrasound-guided supraclavicular block. Methods A double-blind, prospective trial was scheduled for 40 patients of American Society of Anesthesiologists class I–III presenting for upper limb surgery and supraclavicular block. The study dose to be administered was arbitrarily divided into six dose levels (12, 15, 18, 21, 24, and 27 ml) with a priori probabilities of success of 0.5, 0.75, 0.90, 0.95, 0.98, and 0.99 respectively. A continual reassessment method statistical program created a dose–response curve, which would shift direction depending on the success or failure of the block. Our starting dose was 21 ml and the next allocated dose was reestimated by the program to be the dose level with the updated posterior response probability closest to 0.95. Results After recruitment of eight patients, our initial dose levels and associated probabilities were deemed too low to determine the ED95. Updated a prioris were calculated from the statistical program, and the study recommenced with a new starting dose of 30 ml. On completion, the ED95 dose was estimated to be 27 ml (95% CI, 24–28 ml). Conclusions The continual reassessment method trial design provided a credible estimate for the ED95 dose for 0.5% bupivacaine for our technique of supraclavicular block and may be of value as a statistically robust method for dose-finding studies in anesthesiology.

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.

Background The continual reassessment method (CRM) requires an underlying model of the dose-toxicity relationship (“prior skeleton”) and there is limited guidance of what this should be when little is known about this association. In this manuscript the impact of applying the CRM with different prior skeleton approaches and the 3 + 3 method are compared in terms of ability to determine the true maximum tolerated dose (MTD) and number of patients allocated to sub-optimal and toxic doses. Methods Post-hoc dose-escalation analyses on real-life clinical trial data on an early oncology compound (AZD3514), using the 3 + 3 method and CRM using six different prior skeleton approaches. Results All methods correctly identified the true MTD. The 3 + 3 method allocated six patients to both sub-optimal and toxic doses. All CRM approaches allocated four patients to sub-optimal doses. No patients were allocated to toxic doses from sigmoidal, two from conservative and five from other approaches. Conclusions Prior skeletons for the CRM for phase 1 clinical trials are proposed in this manuscript and applied to a real clinical trial dataset. Highly accurate initial skeleton estimates may not be essential to determine the true MTD, and, as expected, all CRM methods out-performed the 3 + 3 method. There were differences in performance between skeletons. The choice of skeleton should depend on whether minimizing the number of patients allocated to suboptimal or toxic doses is more important. Trial registration NCT01162395, Trial date of first registration: July 13, 2010. Electronic supplementary material The online version of this article (doi:10.1186/s12885-016-2702-6) contains supplementary material, which is available to authorized users.

Purpose A dose escalation study to determine the recommended dose (RD) with stereotactic body radiation therapy (SBRT) for peripheral T2N0M0 non-small cell carcinomas (NSCLC) was conducted. The results of the group with PTV⩾100cc are reported in this paper. Materials and methods The continual reassessment method (CRM) was used to determine the dose level that patients should be assigned to and to estimate the maximum tolerated dose (MTD). Dose limiting toxicity (DLT) was Grade 3 or higher radiation pneumonitis (RP), and Grade 2 or higher RP was used as a surrogate DLT. The RD was equal to the MTD. The dose was prescribed at D 95 of the PTV. Results Thirteen patients were accrued. More patients should have been enrolled but we decided not to prolong the study period. No patients experienced Grade 3 RP. Two patients experienced Grade 2 RP at 50Gy in 4 fractions. The predicted MTD was 50.2Gy. The posterior probability of the Grade 2 RP frequency over 40% was 5.3% for the dose level of 50Gy. The RD was determined to be 50Gy. Conclusions The RD was determined to be 50Gy in 4 fractions in this population.

Summary The dose finding problem for single compounds has been generalized to combination therapies and several binary regression models have been proposed in the literature. We propose general tools to evaluate them, in particular dose-free parameterizations of the risks of toxicity and risk ratio functions and plots. New classes of risk functions are also proposed. They generate low dimensional parametric risk functions which are made available to the researcher and commented on for their clinical relevance.

The rate of observed dose-limiting toxicities (DLT) determines the maximum tolerated dose (MTD) in phase I trials. There are cases in which non–drug-related toxicities or other-cause toxicities (OCT) are flagged as DLTs, or vice versa, due to attribution errors. We aim to assess the impact of such errors on the final estimate of MTD. We compared the impact of attribution errors using 2 trial designs—the “3+3” dose-escalation scheme and the continual reassessment method (CRM). Two attribution errors are considered: when a DLT is classified as an OCT (type A error) and when an OCT is misclassified as a DLT (type B error). The impact of these errors on accuracy, patient safety, sample size, and study duration was evaluated by varying the probability of occurrence of each error through simulated trials. Under no errors, CRM is on average 35% more accurate than 3+3 in finding the true MTD. This improved accuracy is maintained in the presence of errors. At a 15% type B error rate, CRM recommends a dose within 2 levels of the true MTD 68% of the time, compared with 17% of the time using the 3+3 method. A DLT must be attributed as an OCT 30% of the time to increase the accuracy of 3+3; otherwise the method recommends a wrong dose approximately 75% of the time. CRM is more robust to toxicity attribution errors compared with the 3+3 as it uses information from all treated patients, leading to a more accurate MTD estimation at the frequency of attribution errors anticipated in phase I clinical trials. Clin Cancer Res; 18(19); 5179–87. ©2012 AACR.

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.

The continual reassessment method (CRM) is a model-based design that aims at finding the maximum tolerated dose (MTD) of a given drug. As the CRM is a model-based technique, its use may be restricted to cases where the true dose-response curve satisfies its underlying working model. Shen and O’Quigley (1996) prove that the CRM is consistent (converges to the MTD) for a family of dose-response curves that satisfies several quite restrictive criteria. Cheung and Chappell (2002) conjecture that the CRM is consistent under a much weaker set of conditions and test their conjecture by a simulation study, but do not provide a formal proof for their claim. The current note fills this gap and provides a formal proof for the conjecture of Cheung and Chappell, thus giving a solid justification for the robustness of the CRM for misspecified model.

The traditional method of 3+3 standard design and model-based Bayesian continual reassessment method (CRM) are commonly used in Phase I clinical trials to identify the maximal tolerated dose(MTD) of a new drug. In this paper we review clinical examples of Phase I trials that were carried out in patients with refractory or relapsed leukemia and myelodysplastic syndrome. The recently proposed 3+1+1 design and rolling-6 design can shorten the trial duration, when a very slow accrual of patients with a simple 3+3 standard design may result in the untimely termination of trials. Too conservative approaches in determining the dose levels in Phase I clinical trials can leave clinical investigators unable to accurately determine the MTD. When determining future patient doses, the designs that use a time-to-event CRM can cooperate late toxicities by accounting for the proportion of the observation period of each enrolled patient. With the CRM design, simulations under different scenarios during the trial are important in detecting the under- or over-estimation of the initial estimate of the dose-limiting toxicity rate for each dose level. We present the advantages and drawbacks of the designs used in Phase I clinical trials for leukemia patients.

Background The Continual Reassessment Method typically is presented as the method of choice for the purpose of dose-finding based on a toxicity scale in phase I clinical trials. However, this adaptive statistical approach also can be applied easily to dose-finding experiments in phase II trials. Purpose To provide a case study from a real clinical trial to illustrate the use of the Continual Reassessment Method in the context of phase II dose finding. Methods The Continual Reassessment Method was used to model the dose-failure relationship in order to estimate the minimal effective dose. This approach was retrospectively used to determine the minimal effective dose of granulocyte colony-stimulating factor for peripheral blood stem cell collection in allografted patients following chemotherapy. Results After the inclusion of 25 patients, the minimal effective dose was estimated to be the third dose level tested in the study. Limitations The main limitation of the Continual Reassessment Method, which is not specific to the method but to the dose-finding setting, is that the empirical choice of the dose range can be either under or over-estimated. The method requires a calibration study prior to trial onset. Conclusions Assuming that a dose-effect relationship is monotonically increasing, the use of the Continual Reassessment Method in phase II dose-finding studies allows the estimation of the minimum effective dose for further studies. Modeling the dose-failure relationship allows the direct use of available software developed for the Continual Reassessment Method in the context of phase I clinical trials.

Background The rolling six design (RSD) is currently being used by the Children’s Oncology Group (COG) as their standard design for Phase I trials. Because the COG has large multi-center trials with fast accrual, the motivation for adopting the RSD is to hasten accrual and shorten the duration of their trials. However, trial suspension due to completion of follow-up still cannot be entirely avoided by the RSD. Therefore, a design that allows continuous enrollment of patients throughout the entire trial is needed. Purpose To demonstrate the superior performance of the Time-to-Event Continual Reassessment Method (TITE-CRM) with continuous patient recruitment relative to the RSD, in terms of identifying the maximum tolerated dose (MTD) and reducing exposure of patients to toxic doses. Methods Using scenarios that were based on an actual pediatric Phase I trial at the University of Michigan, Monte Carlo simulations were used to investigate the operational characteristics of RSD and TITE-CRM. Results The TITE-CRM treated all available patients, identified the MTD more accurately than the RSD and did not increase the probability of exposing patients to toxic doses. Limitations Both the TITE-CRM and RSD assume that the probability of dose limiting toxicity increases with higher dose level. Conclusions The TITE-CRM, which allows for continual enrollment of patients, provides a safe design for pediatric oncology Phase I trials with better accuracy than the RSD.

The continual reassessment method (CRM) is an adaptive model-based design used to estimate the maximum tolerated dose in phase I clinical trials. Asymptotically, the method has been shown to select the correct dose given that certain conditions are satisfied. When sample size is small, specifying a reasonable model is important. While an algorithm has been proposed for the calibration of the initial guesses of the probabilities of toxicity, the calibration of the prior distribution of the parameter for the Bayesian CRM has not been addressed. In this paper, we introduce the concept of least informative prior variance for a normal prior distribution. We also propose two systematic approaches to jointly calibrate the prior variance and the initial guesses of the probability of toxicity at each dose. The proposed calibration approaches are compared with existing approaches in the context of two examples via simulations. The new approaches and the previously proposed methods yield very similar results since the latter used appropriate vague priors. However, the new approaches yield a smaller interval of toxicity probabilities in which a neighboring dose may be selected.

We discuss extensions of model-based designs, such as the continual reassessment method, for use in dose-finding studies. Rather than work with a single model to carry out the design and analysis of a dose-finding study we indicate how the use of several models can greatly increase flexibility. We can appeal to established results on Bayesian model choice and this device makes the inferential problem essentially straightforward. The greater flexibility enables us to take on board many different kinds of added complexity. Examples include extended models to deal with subject heterogeneity, extended models to take account of different treatment schedules and extended models to tackle the problem of partial ordering.

The workshop on the continual reassessment method and related Phase I designs for dose-finding studies was held at Memorial Sloan Kettering Cancer Center in New York on October 2nd, 2009. The workshop was the result of several discussions between Alexia Iasonos and John O'Quigley. These discussions centered around the methodological advancements in Phase I designs with a particular focus on the continual reassessment method (CRM). The participants of the workshop included applied statisticians who have practical experience in using the CRM in clinical trials, an experience gained both in academic and cancer centers, as well as in the pharmaceutical industry in the United States, Japan and Europe. The majority of the participants have participated in Institutional Review Boards and have been involved in discussions with the FDA and European Medicines Agency. One goal of the workshop was to provide a forum to share the lessons learned from finished trials and to discuss the operational and logistical issues, as well as, methodological advancements in adaptive Phase I clinical trial design.

The first human exposure to a new medicine always carries a major risk. Assessment of the safety profile and determination of a therapeutic dose are formidable tasks, particularly in oncology where toxicity is seen as a surrogate for efficacy. Increasing evidence supports the adoption of innovative dose-toxicity models, as these are safer and more efficient in meeting the challenges of modern investigational oncology than traditional models used in Phase I clinical trials.A literature review on dose-toxicity models in oncology was carried out. The objective of this study was to provide a non-mathematical, reader-friendly overview of current and innovative dose-toxicity models in oncology with an emphasis on recent clinical advances, including the benefits of a Bayesian framework.Innovative dose-toxicity models attempt to minimize clinical risk and maximize research performance. Of these, the Bayesian Continual Reassessment Method and the Escalation With Overdose Control are two successful contemporary designs that outperformed traditional models in clinical trials; they account for patient heterogeneity, combination therapy, and they appropriately assess molecularly targeted agents. Support by regulatory authorities is providing an additional incentive to the widespread use of innovative and efficient dose-toxicity designs: this will improve investigational oncology, and ultimately benefit patients and science alike.

The Bayesian continual reassessment method (CRM) and its likelihood version (CRML) provide important tools for the design of Phase I cancer clinical trials. However, a poorly chosen prior distribution in CRM may lead to inferior performance of the method in the early stage of a trial, whereas the maximum-likelihood estimate used in CRML may result in initial high variability. These features of CRM and CRML served as the motivations for the development of this new class of designs, which combines the Bayesian and the likelihood approaches and has CRM and CRML as special cases. Simulation studies on a leukaemia trial show that the proposed class of designs significantly outperforms the traditional up-and-down design.

We study model sensitivity of the continual reassessment method (CRM). The context is that of dose-finding designs where certain design parameters are fixed by the investigator. Although our focus is on the CRM (O'Quigley et al., 1990), the essential ideas can be applied to any sequential dose-finding method. It is expected that different choices of a model family and particular parameterizations will have an impact on performance. Assuming that the constraints outlined in Shen and O'Quigley (1996) are respected, large sample performance is unaffected. However small sample performance will be affected by these choices, which are to some degree arbitrary. This work focuses on the retrospective robustness of the CRM in practice. The question is not of a general theoretical nature where, in the background, we would want to consider large numbers of true potential situations. Instead, the question is raised in the specific context of any actual completed study and is the following: Would we have come to the same conclusion concerning the MTD had we worked with a design specified differently? The sequential nature of the CRM means that this question cannot be answered in any definitive way. We can, though, by appealing to the retrospective CRM (O'Quigley, 2005), provide consistent estimates of the relationships between the MTD and the chosen model. If these estimates suggest that changes in different family model parameters will be accompanied by changes in final recommendation, then we would not be confident in the reliability of the estimated MTD and more work would be needed. Also, of course, at the planning stage, prospective robustness could be studied by simulating trials using particular models and parameterizations.

Novel therapies are challenging the standards of drug development. Agents with specific biologic targets, unknown dose-efficacy curves, and limited toxicity mandate novel designs to identify biologically optimal doses. We review two model-based designs that utilize either a proportional odds model or a continuation ratio model to identify an optimal dose of a single or two-agent combination in a Phase I setting utilizing both toxicity and efficacy data. A continual reassessment method with straightforward dose selection criterion using accumulated data from all patients treated until that time point is employed while allowing for separate toxicity and efficacy curves for each drug in a two-drug setting. The simulation studies demonstrate considerable promise, at least theoretically, in the ability of such model-based designs to identify the optimal dose. Despite such favorable operating characteristics, there are several pragmatic challenges that hinder the routine implementation of such model-based designs in practice. We review and offer practical solutions to potentially overcome some of these challenges. The acceptance and integration of these designs in practice may be quicker and easier if they are developed in concert with a clinical paradigm. Copyright © 2010 John Wiley & Sons, Ltd.

The main goal of phase I cancer clinical trials is to determine the highest dose of a new therapy associated with an acceptable level of toxicity for the use in a subsequent phase II trial. The continual reassessment method (CRM) [O’Quigley, J., Pepe, M., Fisher, L., 1990. Continual reassessment method: a practical design for phase I clinical trials in cancer. Biometrics 46, 33–48] and escalation with overdose control (EWOC) [Babb, J., Rogatko, A., Zacks, S., 1998. Cancer phase I clinical trials: efficient dose escalation with overdose control. Statist. Med. 17 (10), 1103–1120] are two model-based designs used for phase I cancer clinical trials. A few modifications of the (original) CRM and EWOC have been made by many authors. In this paper, we show how CRM and EWOC can be unified and present a hybrid design. We study the characteristics of the approach of the hybrid design. The comparisons of the three designs (CRM, EWOC, and the hybrid design) are presented by convergence rates and overdose proportions. The simulation results show that the hybrid design generally has faster convergence rates than EWOC and smaller overdose proportions than CRM, especially when the true maximum tolerated dose (MTD) is above the mid-level of the dose range considered. The performance of these three designs is also evaluated in terms of sensitivity to outliers.

Background In non-cancer phase II trials, dose-finding trials are usually carried out using fixed designs, in which several doses including a placebo are randomly distributed to patients. However, in certain vulnerable populations, such as neonates or infants, there is an heightened requirement for safety, precluding randomization. Purpose To estimate the minimum effective dose of a new drug from a non-cancer phase II trial, we propose the use of adaptive designs like the Continual Reassessment Method (CRM). This approach estimates the dose closest to some target response, and has been shown to be unbiased and efficient in cancer phase I trials. Methods Based on a motivating example, we point out the individual influence of first outliers in this setting. A weighted version of the CRM is proposed as a theoretical benchmark to control for these outliers. Using simulations, we illustrate how this approach provides further insight into the behavior of the CRM. Results When dealing with low targets like a 10% failure rate, the CRM appears unable to rapidly overcome an early unexpected outcome. This behavior persisted despite changing the inference (Bayesian or likelihood), underlying dose—response model (though slightly improved using the power model), and the number of patients enrolled at each dose level. Limitations The choices for initial guesses of failure rates, the vague prior for the model parameter, and the log-log shape of weights can appear somewhat arbitrary. Conclusions In phase II dose-finding studies in which failure targets are below 20%, the CRM appears quite sensitive to first unexpected outcomes. Using a power model for dose—response improves some behavior if the trial is started at the first dose level and includes at least three to five patients at the starting dose before applying the CRM allocation rule.

This article presents a practical application of a new dose-finding algorithm with a continual reassessment method (CRM) to a phase I study identifying the recommended dose level of chemotherapy in patients with advanced breast cancer. First, we conducted a preliminary study to determine a prior distribution for a model parameter to be used in the CRM, based on prestudy perceptions of a panel of oncologists. During the phase I study, dose-limiting toxicity (DLT) data were continually monitored to guide decisions on dose escalation and de-escalation, based on a CRM using a Bayesian probability model. We analyzed 16 patients, 3 of whom experienced a DLT. The dose-toxicity curve was updated based on observed toxicity data. The recommended doses were very similar to those identified in a previous dose-escalation study using a conventional 3 + 3 cohort design. The main disadvantage of the CRM was the need for dedicated statistical support, but this was outweighed by more accurate estimation of recommended doses.

The continual reassessment method is a recommended dose escalation design for including patients in phase I clinical trials designed to estimate the maximum tolerated dose. However, for a particular trial, the implementation of these methods requires extensive computer programming. Standard 3 + 3 designs do not require this, but have shown to possess poor statistical properties. np1 is a user friendly computer program which has implemented two continual reassessment methods for simulating and conducting a phase I clinical trial. Several options allow the user to investigate operating characteristics under various scenario.

The presence of patient heterogeneity in dose finding studies is inherent (i.e. groups with different maximum tolerated doses). When this type of heterogeneity is not accounted for in the trial design, subjects may be exposed to toxic or suboptimal doses. Options to handle patient heterogeneity include conducting separate trials or splitting the trial into arms. However, cost and/or lack of resources may limit the feasibility of these options. If information is shared between the groups, then both of these options do not benefit from using the shared information. Extending current dose finding designs to handle patient heterogeneity maximizes the utility of existing methods within a single trial. We propose a modification to the time-to-event continual reassessment method to accommodate two groups using a two-parameter model and maximum likelihood estimation. The operating characteristics of the design are investigated through simulations under different scenarios including the scenario where one conducts two separate trials, one for each group, using the one-sample time-to-event continual reassessment method.

We present a Phase I dose escalation trial design based on a modified continual reassessment method that allows for sharing of information between populations. We describe our approach in the context of a trial for patients with acute lymphoblastic leukemia (ALL) that is currently being conducted. The ALL trial enrolls both adult and pediatric patient populations. Dose escalation and the determination of the maximum tolerated dose (MTD) are performed separately for each population, but to increase efficiency, information about the dose–toxicity curve is shared. Dose escalation rules allow pediatric patients to skip dose levels provided safety has been shown in adults and the dose level is estimated to be safe for pediatric patients. Trial objectives are to efficiently determine the MTD for each population and to minimize the number of pediatric patients required for dose escalation.

Background and objectives Dose finding trials using model-based methods have the ability to handle the increasingly complex landscape being seen in clinical trials. Issues such as patient heterogeneity in trial populations are important to address in the designing of a trial in addition to the inclusion/exclusion criteria. Designs accommodating patient heterogeneity have been described using the continual reassessment method (CRM) and time-to-event CRM (TITE-CRM), yet, the implementation of these trials in practice have been limited. These methods and other model-based methods generally need statisticians to help design and conduct these trials. However, the statistical programs which facilitate the use of these methods, currently available focus on estimation in the one-sample case. Methods A SAS program to accommodate two groups using the TITE-CRM and likelihood estimation has been developed. The program consists of macros that assist with the planning and implementation of a trial accounting for patient heterogeneity. Results Description of the program is given as well as examples using the programs. For planning purposes, an example will be provided showing how the program can be used to guide sample size estimates for the trial. Conclusions This program provides researchers with a valuable tool for designing dose-finding studies to account for the presence of patient heterogeneity and conduct a trial using a hypothetical example.

I review the designs available for Phase I dose-finding studies of chemotherapeutic agents in cancer patients. The designs are based on the assumption that both efficacy and toxicity increase with dose, and thus attempt to minimize the number of patients treated at low doses, and also to minimize the chance that patients will be treated at excessively toxic or lethal doses. The designs fall into two classes: rule-based and model-guided. Rule-based designs can always determine a reasonable maximum tolerable dose based on observed toxicity, but when model assumptions are not satisfied, many model-guided designs will not.

We consider the case of phase I trials for treatment of cancer or other severe diseases in which grade information is available about the severity of toxicity. Most dose allocation procedures dichotomize toxicity grades based on being dose limiting, which may not work well for severe and possibly irreversible toxicities such as renal, liver, and neurological toxicities, or toxicities with long duration. We propose a simple extension to the continual reassessment method (CRM), called the Quasi-CRM, to incorporate grade information. Toxicity grades are first converted to numeric scores that reflect their impacts on the dose allocation procedure, and then incorporated into the CRM using the quasi-Bernoulli likelihood. A simulation study demonstrates that the Quasi-CRM is superior to the standard CRM and comparable to a univariate version of the Bekele and Thall method (2004, Journal of the American Statistical Association 99, 26-35). We also present sensitivity analysis of the new method with respect to toxicity scores, and discuss practical issues such as extending the simple algorithmic up-and-down designs.