Adaptive Dose-finding Designs Research Articles

Implementation of adaptive dose-finding designs in two early phase haematological trials: clinical, operational, and methodological challenges

The majority of Phase I dose-finding trials in oncology have been dominated by the traditional up-and-down designs, such as 3+3 designs. However, in recent years, there is an emerging interest in more innovative model-based dose finding methods such as the Continual Reassessment Method (CRM), though examples of implementing such adaptive designs in the medical literature remain limited. In this paper, we discuss three main challenges faced in the implementation of CRM in a Phase I trial in Acute Myeloid Leukaemia and a Phase I/II trial in T-cell Lymphoma, and possible solutions. Despite the mounting evidence of the CRM's superior performance in identifying the maximum tolerated dose, it remains a challenge to communicate to clinicians the rationale for using a method with such a greater level of statistical complexity. Operational challenges include trialists' impression that dose-recommendations come from a black-box, which contrast unfavourably with the transparent simple rules of a 3+3 design; and concerns that possible delays that might result. Methodological challenges include the different variants of a CRM design that could be considered, both from Bayesian and Maximum Likelihood frameworks, and how to choose an appropriate design that could be adapted to specific requirements of a trial. We discuss proposed solutions to challenges encountered and introduce the use of Dose Transition Sample Paths, demonstrating how this can be used to aid understanding in the working of such adaptive designs. We will show that it is a valuable planning tool to achieve a seamless, clear transition for each dose-recommendation.

Adaptive dose-finding designs to identify multiple doses that achieve multiple response targets

The objective of the DILT1D trial is to identify doses of interleukin-2 that achieve targeted increases in the T regulatory cell population in recently diagnosed type 1 diabetes. DILT1D aims to accurately identify a minimally effective dose and a maximal dose in a limited number of patients (40) that may be repeatedly administered in later phase trials. The dose is administered subcutaneously so can be chosen from a continuous range up to a limit chosen on tolerability grounds. The design has an initial learning phase where pairs of patients are assigned to five pre-assigned doses. The next phase is fully sequential with an interim analysis after each patient to determine the choice of dose based on the optimality criterion to minimise the determinant of the covariance of the estimated target doses. The dose-choice algorithm assumes that a specific parametric dose-response model is the true relationship, and so a variety of models are considered at the interim and human judgement involved in the overall decision. Simulation studies found that having two targets and assuming a model with a small number of parameters (≤3) leads to a choice of doses that are not near to the target doses. Fitting models with greater flexibility with more parameters (≥4) results in a choice of doses near to the target doses. Overall the design is efficient and seamlessly combines the initial learning and subsequent confirmatory stages.

Dose‐finding design using mixed‐effect proportional odds model for longitudinal graded toxicity data in phase I oncology clinical trials

Phase I oncology clinical trials are designed to identify the optimal dose that will be recommended for phase II trials. This dose is typically defined as the dose associated with a certain probability of severe toxicity during the first cycle of treatment, although toxicity is repeatedly measured over cycles on an ordinal scale. We propose a new adaptive dose-finding design using longitudinal measurements of ordinal toxic adverse events, with proportional odds mixed-effect models. Likelihood-based inference is implemented. The optimal dose is then the dose producing a target rate of severe toxicity per cycle. This model can also be used to identify cumulative or late toxicities. The performances of this approach were compared with those of the continual reassessment method in a simulation study. Operating characteristics were evaluated in terms of correct identification of the target dose, distribution of the doses allocated and power to detect trends in the risk of toxicities over time. This approach was also used to reanalyse data from a phase I oncology trial. Use of a proportional odds mixed-effect model appears to be feasible in phase I dose-finding trials, increases the ability of selecting the correct dose and provides a tool to detect cumulative effects.

A Bayesian Dose-finding Design for Drug Combination Trials with Delayed Toxicities.

We propose a Bayesian adaptive dose-finding design for drug combination trials with delayed toxicity. We model the dose-toxicity relationship using the Finney model, a model widely used in drug-drug interaction studies. The intuitive interpretations of the Finney model facilitate incorporating the available prior dose-toxicity information from single-agent trials into combination trials through prior elicitation. We treat unobserved delayed toxicity outcomes as missing data and handle them using Bayesian data augmentation. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various practical scenarios. Results show that the proposed design is safe and able to select the target dose combinations with high probabilities.

Bayesian Dose Finding for Combined Drugs with Discrete and Continuous Doses

The trend of treating patients with combined drugs has grown in cancer clinical trials. Often, evaluating the synergism of multiple drugs is the primary motivation for such drug-combination studies. To enhance the patient response, a new cancer therapeutic agent is often investigated together with an existing standard of care (SOC) agent. At least a certain amount of dosage of the SOC is administered in order to maintain some therapeutic effects in patients. For clinical trials involving a continuous-dose SOC and a discrete-dose agent, we propose a two-stage Bayesian adaptive dose-finding design. The first stage takes a continual reassessment method to locate the appropriate dose for the discrete-dose agent while fixing the continuous-dose SOC at the minimal therapeutic dose. In the second stage, we make a fine dose adjustment by calibrating the continuous dose to achieve the target toxicity rate as closely as possible. Dose escalation or de-escalation is based on the posterior estimates of the joint toxicity probabilities of combined doses. As the toxicity data accumulate during the trial, we adaptively assign each cohort of patients to the most appropriate dose combination. We conduct extensive simulation studies to examine the operating characteristics of the proposed two-stage design and demonstrate the design's good performance with practical scenarios.

A Two-Stage Adaptive Design in Phase 2 Clinical Trials for Acute Treatment of Migraine

In the development of acute migraine treatments, traditional phase 2 dose-finding trial designs are particularly challenging when a wide dose range needs to be investigated. In this article, an adaptive two-stage dose-finding design is introduced. The proposed design combines traditional phase 2a and 2b to serve the dual purpose of proof of concept and dose finding through the use of an unblinded interim analysis that provides an opportunity for adaptation. The design has a fixed total sample size but includes provisions for discontinuation of less effective doses at the interim analysis to allow for the allocation of patients into a more promising range of doses. The interim adaptation is designed to increase the amount of information collected on more effective doses and increase study power as compared to a traditional nonadaptive dose-finding design. On the other hand, despite the interim look and adaptation of the design, conventional statistical analyses of the final data can still be used for the final analysis and provide proper inferences with adequately controlled type I error and an ignorable amount of bias.

Model-Based Bayesian Adaptive Dose-Finding Designs for a Phase II Trial

We describe the planning of a dose-finding study for a compound currently in early Phase II clinical development. Data from a trial with the same primary endpoint are available for a marketed drug that has the same pharmacological mechanism, which provides strong prior information for some characteristics of the new compound. We develop and evaluate informative model-based Bayesian analyses. We also evaluate adaptive designs that change the allocation of patients to doses after an interim analysis. The performance of the model-based Bayesian analyses applied to the adaptive designs is compared to the performance of corresponding analyses applied to a nonadaptive design. The performance is also compared to model-based maximum likelihood estimation and simple pairwise comparison of treatment group means applied to the nonadaptive design to assess the contributions of the model, Bayesian prior distribution, and adaptive designs to improvements in decision and estimation performance.

Benefits, challenges and obstacles of adaptive clinical trial designs.

In recent years, the use of adaptive design methods in pharmaceutical/clinical research and development has become popular due to its flexibility and efficiency for identifying potential signals of clinical benefit of the test treatment under investigation. The flexibility and efficiency, however, increase the risk of operational biases with resulting decrease in the accuracy and reliability for assessing the treatment effect of the test treatment under investigation. In its recent draft guidance, the United States Food and Drug Administration (FDA) expresses regulatory concern of controlling the overall type I error rate at a pre-specified level of significance for a clinical trial utilizing adaptive design. The FDA classifies adaptive designs into categories of well-understood and less well-understood designs. For those less well-understood adaptive designs such as adaptive dose finding designs and two-stage phase I/II (or phase II/III) seamless adaptive designs, statistical methods are not well established and hence should be used with caution. In practice, misuse of adaptive design methods in clinical trials is a concern to both clinical scientists and regulatory agencies. It is suggested that the escalating momentum for the use of adaptive design methods in clinical trials be slowed in order to allow time for development of appropriate statistical methodologies.

A latent contingency table approach to dose finding for combinations of two agents.

Two-agent combination trials have recently attracted enormous attention in oncology research. There are several strong motivations for combining different agents in a treatment: to induce the synergistic treatment effect, to increase the dose intensity with nonoverlapping toxicities, and to target different tumor cell susceptibilities. To accommodate this growing trend in clinical trials, we propose a Bayesian adaptive design for dose finding based on latent 2 x 2 tables. In the search for the maximum tolerated dose combination, we continuously update the posterior estimates for the unknown parameters associated with marginal probabilities and the correlation parameter based on the data from successive patients. By reordering the dose toxicity probabilities in the two-dimensional space, we assign each coming cohort of patients to the most appropriate dose combination. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various practical scenarios. Finally, we illustrate our dose-finding procedure with a clinical trial of agent combinations at M. D. Anderson Cancer Center.

Bayesian Dose Finding in Oncology For Drug Combinations by Copula Regression

Summary Treating patients with a combination of agents is becoming commonplace in cancer clinical trials, with biochemical synergism often the primary focus. In a typical drug combination trial, the toxicity profile of each individual drug has already been thoroughly studied in single-agent trials, which naturally offers rich prior information. We propose a Bayesian adaptive design for dose finding that is based on a copula-type model to account for the synergistic effect of two or more drugs in combination. To search for the maximum tolerated dose combination, we continuously update the posterior estimates for the toxicity probabilities of the combined doses. By reordering the dose toxicities in the two-dimensional probability space, we adaptively assign each new cohort of patients to the most appropriate dose. Dose escalation, de-escalation or staying at the same doses is determined by comparing the posterior estimates of the probabilities of toxicity of combined doses and the prespecified toxicity target. We conduct extensive simulation studies to examine the operating characteristics of the design and illustrate the proposed method under various practical scenarios.

An adaptive dose-finding design incorporating both toxicity and efficacy

Novel therapies are challenging the standards of drug development. Agents with specific biologic targets and limited toxicity require novel designs to determine doses to be taken forward into larger studies. In this paper, we describe an approach that incorporates both toxicity and efficacy data into the estimation of the biologically optimal dose of an agent in a phase I trial. The approach is based on the flexible continuation-ratio model, and uses straightforward optimal dose selection criteria. Dose selection is based on all patients treated up until that time point, using a continual reassessment method approach. Dose-outcome curves considered include monotonically increasing, monotonically decreasing, and unimodal curves. Our simulation studies demonstrate that the proposed design, which we call TriCRM, has favourable operating characteristics.