Bridging continual reassessment method for phase I clinical trials in different ethnic populations

Accumulating evidence shows that the conventional one‐size‐fits‐all dose‐finding paradigm is problematic when applied to different ethnic populations. Because of inter‐ethnic heterogeneity, the dosage established in a landmark trial for a certain population may not be generalizable to a different ethnic population, and a follow‐up bridge trial is often needed to find the maximum tolerated dose for the new population. We propose the bridging continual reassessment method (B‐CRM) to facilitate dose finding for such follow‐up bridge trials. The B‐CRM borrows information from the landmark trial through a novel estimate of the dose‐toxicity curve and accommodates the inter‐ethnic heterogeneity using the Bayesian model averaging approach. Extensive simulation studies show that the B‐CRM has desirable operating characteristics with a high probability to select the target dose. This article focuses on ethnic heterogeneity, but the proposed method can be directly used to handle other types of patient heterogeneity, for example, patient subgroups defined by prognostic factors or biomarkers. The software to implement the B‐CRM design is available for free download at http://odin.mdacc.tmc.edu/~yyuan/ . Copyright © 2015 John Wiley & Sons, Ltd.     

A trivariate continual reassessment method for phase I/II trials of toxicity,efficacy, and surrogate efficacy

Recently, many Bayesian methods have been developed for dose finding when simultaneously modeling both toxicity and efficacy outcomes in a blended phase I/II fashion. A further challenge arises when all the true efficacy data cannot be obtained quickly after the treatment so that surrogate markers are instead used (e.g., in cancer trials). We propose a framework to jointly model the probabilities of toxicity, efficacy, and surrogate efficacy given a particular dose. Our trivariate binary model is specified as a composition of two bivariate binary submodels. In particular, we extend the bivariate continual reassessment method (CRM), as well as utilize a particular Gumbel copula. The resulting trivariate algorithm utilizes all the available data at any given time point and can flexibly stop the trial early for either toxicity or efficacy. Our simulation studies demonstrate that our proposed method can successfully improve dosage targeting efficiency and guard against excess toxicity over a variety of true model settings and degrees of surrogacy. Copyright © 2012 John Wiley & Sons, Ltd.     

Practical modifications to the time-to-event continual reassessment method for phase I cancer trials with fast patient accrual and late-onset toxicities

The goal of phase I cancer trials is to determine the highest dose of a treatment regimen with an acceptable toxicity rate. Traditional designs for phase I trials, such as the Continual Reassessment Method (CRM) and the 3 + 3 design, require each patient or a cohort of patients to be fully evaluated for the dose-limiting toxicity (DLT) before new patients can be enrolled. As such, the trial duration may be prohibitively long. The Time-to-Event Continual Reassessment Method (TITE-CRM, Cheung and Chappell, 2000) circumvents this limitation by allowing staggered patient accrual without the need for complete DLT follow-up of previously treated patients. However, in the setting of fast patient accrual and late-onset toxicities, the TITE-CRM results in overly aggressive dose escalation and exposes a considerable number of patients to toxic doses. We examine a modification to the TITE-CRM proposed by the original TITE-CRM creator and propose an alternative approach useful in this setting by incorporating an accrual suspension rule. A simulation study designed based on a neuro-oncology trial indicates that the modified methods provide a much improved degree of safety than the TITE-CRM while maintaining desirable design accuracy. The practical aspects of the proposed designs are discussed. The modifications presented are useful when planning phase I trials involving chemoradiation therapy.     

Adaptive prior variance calibration in the Bayesian continual reassessment method

The use of the continual reassessment method (CRM) and other model‐based approaches to design Phase I clinical trials has increased owing to the ability of the CRM to identify the maximum tolerated dose better than the 3 + 3 method. However, the CRM can be sensitive to the variance selected for the prior distribution of the model parameter, especially when a small number of patients are enrolled. Although methods have emerged to adaptively select skeletons and to calibrate the prior variance only at the beginning of a trial, there has not been any approach developed to adaptively calibrate the prior variance throughout a trial. We propose three systematic approaches to adaptively calibrate the prior variance during a trial and compare them via simulation with methods proposed to calibrate the variance at the beginning of a trial. Copyright © 2012 John Wiley & Sons, Ltd.     

Calibration of prior variance in the Bayesian continual reassessment method

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.     

Application of the continual reassessment method to a phase I dose-finding trial in Japanese patients: East meets West

After cancer-related phase I dose-finding trials are completed in Western countries, further phase I trials are often conducted to determine recommended doses (RDS) for Japanese patients. This may be due to concerns about possible differences in treatment tolerability between Caucasians and Japanese. In most of these, a conventional '3 +3' cohort study design is used in making dose escalation decisions, possibly due to its relatively easy implementation. Since its proposal by O'Quigleybiet al. (1990; Biometrics, 46:33-48), the continual reassessment method (CRM) has been used increasingly in cancer-related phase I dose-finding studies as an alternative to '3 +3' designs. One of the principal advantages of applying a Bayesian CRM may be the utilization of all available prior information to estimate RDS through prior distributions that are assumed for model parameters representing the dose-toxicity relationship. In this paper, we present an application of the Bayesian CRM to a phase I dose-finding study in Japanese patients with advanced breast cancer using an informative prior elicited from clinical investigators. In some settings, it may be appropriate to use an informative prior that reflects the accurate and comprehensive previous knowledge of clinical investigators. On the other hand, for a model-based Bayesian outcome-adaptive clinical trial, it is necessary to establish sufficiently vague priors so that accumulating data dominate decisions as the amount of observed data increases. Thus, we retrospectively investigated the relative strength of the prior using a recently proposed method to compute a prior effective sample size.     

Adaptive dose finding for phase I clinical trials of drugs used for chemotherapy of cancer

Phase I clinical trials of cancer chemotherapy drugs are intended to determine the maximum tolerable dose (MTD). Thestandard method employed is a rule-based dose-escalation scheme in which escalation depends on the number of patients at a dose level that have dose-limiting toxicity (DLT). The MTD is thus defined in terms of the rules and a series of dose levels selected for sampling. For some trials it is desirable to have a more precise definition of the MTD, and to determine the MTD more accurately than possible with the rule-based schemes. Continuous reassessment methods (CRMs) define the MTD to be the dose at which a fixed fraction of patients experience DLT, and thus appear suited to these trials. It is shown, however, that these methods can have failure modes that in fact make them unattractive. An alternative data-driven dose-finding method is described that combines the robustness of the rule-based methods and with features of CRMs. The method has two stages. In the first stage, doses are escalated by a factor of 1.5. In the second stage, which begins at the first instance of DLT, a two-parameter logistic dose-response model estimates the MTD from the DLT experience of all patients. The model is initialized by setting the dose (d10) at which 10 per cent of patients would experience DLT to half the dose at which the first DLT was observed, and the dose (d90) at which 90 per cent would experience DLT to ten times d10. Weights are assigned such that the information at d10 and d90 is equivalent to that of one patient at each of the two doses. Cohorts of three patients are treated in both stages, and the dose for a new cohort in the second stage is the estimated MTD. The only prior information required to specify the design completely is the dose which will be given to the first cohort. Two stopping rules are investigated; among the requirements for these are that at least three (or four) DLTs be observed and at least nine patients be treated in the second stage. Simulations show that a coefficient of variation of approximately 0.4 on a target DLT probability of 0.3 is obtained over a wide variation in dose-response characteristics of the study drug. The performance of the new method is compared to that of rule-based methods.     

The continual reassessment method in cancer phase i clinical trials: A simulation study

In cancer studies, the aim of phase I clinical trials is to identify an appropriate dose for experimentation in phase II and III studies. The continual reassessment method (CRM) has been developed recently and presented as the method of choice in the design and analysis of such phase I studies. However, to implement the method, some methodological and practical considerations must be addressed. This paper examines, through a simulation study, the sensitivity of CRM both to the initial modelling of the dose-toxicity relationship and the prior. It appears that the performance of CRM can be improved by using vague priors and initial tuning of the model to allow flexibility.     

An extension of the continual reassessment method using decision theory.

The primary goal of a phase I trial is to find the maximally tolerated dose (MTD) of a treatment. The MTD is usually defined in terms of a tolerable probability, q(*), of toxicity. Our objective is to find the highest dose with toxicity risk that does not exceed q(*), a criterion that is often desired in designing phase I trials. This criterion differs from that of finding the dose with toxicity risk closest to q(*), that is used in methods such as the continual reassessment method. We use the theory of decision processes to find optimal sequential designs that maximize the expected number of patients within the trial allocated to the highest dose with toxicity not exceeding q(*), among the doses under consideration. The proposed method is very general in the sense that criteria other than the one considered here can be optimized and that optimal dose assignment can be defined in terms of patients within or outside the trial. It includes as an important special case the continual reassessment method. Numerical study indicates the strategy compares favourably with other phase I designs.     

The continual reassessment method and its applications: a Bayesian methodology for phase I cancer clinical trials

We discuss the continual reassessment method (CRM) and its extension with practical applications in phase I and I/II cancer clinical trials. The CRM has been proposed as an alternative design of a traditional cohort design and its essential features are the sequential (continual) selection of a dose level for the next patients based on the dose-toxicity relationship and the updating of the relationship based on patients' response data using Bayesian calculation. The original CRM has been criticized because it often tends to allocate too toxic doses to many patients and our proposal for overcoming this practical problem is to monitor a posterior density function of the occurrence of the dose limiting toxicity (DLT) at each dose level. A simulation study shows that strategies based on our proposal allocate a smaller number of patients to doses higher than the maximum tolerated dose (MTD) compared with the original method while the mean squared error of the probability of the DLT occurrence at the MTD is not inflated. We present a couple of extensions of the CRM with real prospective applications: (i) monitoring efficacy and toxicity simultaneously in a combination phase I/II trial; (ii) combining the idea of pharmacokinetically guided dose escalation (PKGDE) and utilization of animal toxicity data in determining the prior distribution. A stopping rule based on the idea of separation among the DLT density functions is discussed in the first example and a strategy for determining the model parameter of the dose-toxicity relationship is suggested in the second example.     

A design for phase I trials in completely or partially ordered groups

We propose a design for dose finding for cytotoxic agents in completely or partially ordered groups of patients. By completely ordered groups, we mean that prior to the study, there is clinical information that would indicate that for a given dose, the groups can be ordered with respect to the probability of toxicity at that dose. With partially ordered groups, at a given dose, only some of the groups can be ordered with respect to the probability of toxicity at that dose. The method we propose includes elements of the parametric model used in the continual reassessment method combined with the Hwang‐Peddada order‐restricted estimation procedure. We evaluate the operating characteristics of these designs in a family of dose–toxicity curves representing complete and partial orders. Copyright © 2017 John Wiley & Sons, Ltd.     

Adaptive dose modification for phase I clinical trials

Most phase I dose‐finding methods in oncology aim to find the maximum‐tolerated dose from a set of prespecified doses. However, in practice, because of a lack of understanding of the true dose–toxicity relationship, it is likely that none of these prespecified doses are equal or reasonably close to the true maximum‐tolerated dose. To handle this issue, we propose an adaptive dose modification (ADM) method that can be coupled with any existing dose‐finding method to adaptively modify the dose, when it is needed, during the course of dose finding. To reflect clinical practice, we divide the toxicity probability into three regions: underdosing, acceptable, and overdosing regions. We adaptively add a new dose whenever the observed data suggest that none of the investigational doses are likely to be located in the acceptable region. The new dose is estimated via a nonparametric dose–toxicity model based on local polynomial regression. The simulation study shows that ADM substantially outperforms the similar existing method. We applied ADM to a phase I cancer trial. Copyright © 2016 John Wiley & Sons, Ltd.     

Some practical improvements in the continual reassessment method for phase I studies

The Continual Reassessment Method (CRM) is a Bayesian phase I design whose purpose is to estimate the maximum tolerated dose of a drug that will be used in subsequent phase II and III studies. Its acceptance has been hindered by the greater duration of CRM designs compared to standard methods, as well as by concerns with excessive experimentation at high dosage levels, and with more frequent and severe toxicity. This paper presents the results of a simulation study in which one assigns more than one subject at a time to each dose level, and each dose increase is limited to one level. We show that these modifications address all of the most serious criticisms of the CRM, reducing the duration of the trial by 50–67 per cent, reducing toxicity incidence by 20–35 per cent, and lowering toxicity severity. These are achieved with minimal effects on accuracy. Most important, based on our experience at our institution, such modifications make the CRM acceptable to clinical investigators.     

Heterogeneity in phase I clinical trials: prior elicitation and computation using the continual reassessment method

Heterogeneity in a phase I clinical trial patient population may lead to distinctly different dose-response relationships along covariate values. For a given target probability of toxicity, this implies different maximum tolerated doses (MTDs) for each distinct subpopulation. Within the framework of O'Quigley, Pepe and Fisher's (1990) continual reassessment method, we propose the notion of average and patient-specific MTDs by augmenting the dose--response model with other covariates to account for such differences. A method to elicit prior distributions on the dose and other covariate parameters are proposed, based on the predictive approach of Ibrahim and Laud (1994), Laud and Ibrahim (1995), and Ibrahim, Ryan and Chen (1998). This approach relies on prior predictions for the response vector y(0) and a quantity a(0) specifying uncertainty in y(0). Then, y(0) and a(0) are used to specify a prior for the regression coefficients in a semi-automatic fashion. The elicitation scheme for y(0) uses results from previous phase I cancer clinical trials. The average and patient-specific MTDs and an elicitation method are demonstrated in logistic regression examples.     

Adaptive design improvements in the continual reassessment method for phase I studies.

The continual reassessment method (CRM) enables full and efficient use of all data and prior information available in a phase I study. However, despite a number of recent enhancements to the method, its acceptance in actual clinical practice has been hampered by several practical difficulties. In this paper, we consider several further refinements in the context of phase I oncology trials. In particular, we allow the trial to stop when the width of the posterior 95 per cent probability interval for the maximum tolerated dose (MTD) becomes sufficiently narrow (that is, when the information accumulating from the trial data reaches a prespecified level). We employ a simulation study to evaluate five such stopping rules under three alternative states of prior knowledge regarding the MTD (accurate, too low and too high). Our results suggest our adaptive designs preserve the CRM's estimation ability while offering the possibility of earlier stopping of the trial.     

A Bayesian dose finding design for dual endpoint phase I trials

We propose a dose-finding weighted design for an early clinical trial which aims to determine the optimal dose, selected on the basis of both efficacy and toxicity, to be used in patients entering subsequent studies in a drug development process. The goal is to identify the optimal dose, while using a minimal number of subjects. For each dose under test, a decision table is defined with a utility value attached to each possible decision. The relationship between the utility and the target probability for each outcome is shown. A Dirichlet prior is used and we illustrate the process of maximizing the expected utility under the resulting posterior distribution to find the optimal decision at each stage of the trial. We show how this affects the eventual choice of optimal dose in various scenarios. Properties of our design are discussed and compared with a current standard design.     

Improved up-and-down designs for phase I trials

We consider several designs from the family of up-and-down rules for the sequential allocation of dose levels to subjects in a dose-response study. We show that an up-and-down design can be improved by using more information than the most recent response. For example, the k-in-a-row rule uses up to the k most recent responses. We introduce a new design, the Narayana rule, which uses a local estimate of the probability of toxicity calculated from all previous responses. For the Narayana rule, as the sample size gets large, the probability of assignment goes to zero for dose levels not among the two (or three) closest to the target. Different estimators of the target dose are compared. We find that the isotonic regression estimator is superior to other estimators for small to moderate sample sizes.     

An adaptive dose‐finding method using a change‐point model for molecularly targeted agents in phase I trials

The paradigm of oncology drug development is expanding from developing cytotoxic agents to developing biological or molecularly targeted agents (MTAs). Although it is common for the efficacy and toxicity of cytotoxic agents to increase monotonically with dose escalation, the efficacy of some MTAs may exhibit non‐monotonic patterns in their dose–efficacy relationships. Many adaptive dose‐finding approaches in the available literature account for the non‐monotonic dose–efficacy behavior by including additional model parameters. In this study, we propose a novel adaptive dose‐finding approach based on binary efficacy and toxicity outcomes in phase I trials for monotherapy using an MTA. We develop a dose–efficacy model, the parameters of which are allowed to change in the vicinity of the change point of the dose level, in order to consider the non‐monotonic pattern of the dose–efficacy relationship. The change point is obtained as the dose that maximizes the log‐likelihood of the assumed dose–efficacy and dose‐toxicity models. The dose‐finding algorithm is based on the weighted Mahalanobis distance, calculated using the posterior probabilities of efficacy and toxicity outcomes. We compare the operating characteristics between the proposed and existing methods and examine the sensitivity of the proposed method by simulation studies under various scenarios. Copyright © 2016 John Wiley & Sons, Ltd.     

Escalation with overdose control for phase I drug‐combination trials

Dose finding for combined drugs has grown rapidly in oncology drug development. The escalation with overdose control (EWOC) method is a popular model‐based dose‐finding approach to single‐agent phase I clinical trials. When two drugs are combined as a treatment, we propose a two‐dimensional EWOC design for dose finding on the basis of a four‐parameter logistic regression model. During trial conduct, we continuously update the posterior distribution of the maximum tolerated dose (MTD) combination to find the most appropriate dose combination for each cohort of patients. The probability that the next assigned dose combination exceeds the MTD combination can be controlled by a feasibility bound, which is based on a prespecified quantile level of the MTD distribution such as to reduce the possibility of overdosing. We determine dose escalation, de‐escalation, or staying at the same doses by searching the MTD combination along the rows and columns in a two‐drug combination matrix, respectively. We conduct simulation studies to examine the performance of the two‐dimensional EWOC design under various practical scenarios, and illustrate it with a trial example. Copyright © 2013 John Wiley & Sons, Ltd.     

Using the time‐to‐event continual reassessment method in the presence of partial orders

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. Copyright © 2012 John Wiley & Sons, Ltd.