Posterior maximization and averaging for Bayesian working model choice in the continual reassessment method

Phase I study of opaganib, an oral sphingosine kinase 2-specific inhibitor, in relapsed and/or refractory multiple myeloma

Multiple myeloma (MM) remains an incurable disease and there is an unmet medical need for novel therapeutic drugs that do not share similar mechanisms of action with currently available agents. Sphingosine kinase 2 (SK2) is an innovative molecular target for anticancer therapy. We previously reported that treatment with SK2 inhibitor opaganib inhibited myeloma tumor growth in vitro and in vivo in a mouse xenograft model. In the current study, we performed a phase I study of opaganib in patients with relapsed/refractory multiple myeloma (RRMM). Thirteen patients with RRMM previously treated with immunomodulatory agents and proteasome inhibitors were enrolled and treated with single-agent opaganib at three oral dosing regimens (250 mg BID, 500 mg BID, or 750 mg BID, 28 days as a cycle). Safety and maximal tolerated dose (MTD) were determined. Pharmacokinetics, pharmacodynamics, and correlative studies were also performed. Opaganib was well tolerated up to a dose of 750 mg BID. The most common possibly related adverse event (AE) was decreased neutrophil counts. There were no serious AEs considered to be related to opaganib. MTD was determined as at least 750 mg BID. On an intent-to-treat basis, one patient (7.7%) in the 500 mg BID dose cohort showed a very good partial response, and one other patient (7.7%) achieved stable disease for 3 months. SK2 is an innovative molecular target for antimyeloma therapy. The first-in-class SK2 inhibitor opaganib is generally safe for administration to RRMM patients, and has potential therapeutic activity in these patients. Clinicaltrials.gov: NCT02757326.

A nonparametric Bayesian method for dose finding in drug combinations cancer trials

We propose an adaptive design for early-phase drug-combination cancer trials with the goal of estimating the maximum tolerated dose (MTD). A nonparametric Bayesian model, using beta priors truncated to the set of partially ordered dose combinations, is used to describe the probability of dose limiting toxicity (DLT). Dose allocation between successive cohorts of patients is estimated using a modified continual reassessment scheme. The updated probabilities of DLT are calculated with a Gibbs sampler that employs a weighting mechanism to calibrate the influence of data vs the prior. At the end of the trial, we recommend one or more dose combinations as the MTD based on our proposed algorithm. We apply our method to a Phase I clinical trial of CB-839 and Gemcitabine that motivated this nonparametric design. The design operating characteristics indicate that our method is comparable with existing methods.

A placebo-controlled Bayesian dose finding design based on continuous reassessment method with application to stroke research

Traditional dose-finding designs do not require assignment of patients to a control group. Motivated by SHRINC (Safety of Pioglitazone for hematoma resolution in intracerebral hemorrhage), we developed a placebo-controlled dose-finding study to identify the maximum tolerated dose for pioglitazone in stroke patients with spontaneous intracerebral hemorrhage. We designed an extension of the continuous reassessment method that allowed to incorporate information from the control group (i.e., the standard of care), and utilized it to determine the maximum tolerated dose in the SHRINC trial. We evaluated the operating characteristics of our design by conducting extensive simulation studies. Our findings from the simulation studies demonstrate that our proposed design is robust and performs well. By estimating the toxicity rate in the control group, we were able to obtain more accurate information about the natural history of the disease and identify appropriate dose for the next phase of this study. The proposed design provides a tool to incorporate the information from the control group into the dose-finding framework for trials with similar objectives.

Unified approach for extrapolation and bridging of adult information in early-phase dose-finding paediatric studies

The number of trials conducted and the number of patients per trial are typically small in paediatric clinical studies. This is due to ethical constraints and the complexity of the medical process for treating children. While incorporating prior knowledge from adults may be extremely valuable, this must be done carefully. In this paper, we propose a unified method for designing and analysing dose-finding trials in paediatrics, while bridging information from adults. The dose-range is calculated under three extrapolation options, linear, allometry and maturation adjustment, using adult pharmacokinetic data. To do this, it is assumed that target exposures are the same in both populations. The working model and prior distribution parameters of the dose–toxicity and dose–efficacy relationships are obtained using early-phase adult toxicity and efficacy data at several dose levels. Priors are integrated into the dose-finding process through Bayesian model selection or adaptive priors. This calibrates the model to adjust for misspecification, if the adult and pediatric data are very different. We performed a simulation study which indicates that incorporating prior adult information in this way may improve dose selection in children.

A default method to specify skeletons for Bayesian model averaging continual reassessment method for phase I clinical trials

The Bayesian model averaging continual reassessment method (CRM) is a Bayesian dose-finding design. It improves the robustness and overall performance of the continual reassessment method (CRM) by specifying multiple skeletons (or models) and then using Bayesian model averaging to automatically favor the best-fitting model for better decision making. Specifying multiple skeletons, however, can be challenging for practitioners. In this paper, we propose a default way to specify skeletons for the Bayesian model averaging CRM. We show that skeletons that appear rather different may actually lead to equivalent models. Motivated by this, we define a nonequivalence measure to index the difference among skeletons. Using this measure, we extend the model calibration method of Lee and Cheung (2009) to choose the optimal skeletons that maximize the average percentage of correct selection of the maximum tolerated dose and ensure sufficient nonequivalence among the skeletons. Our simulation study shows that the proposed method has desirable operating characteristics. We provide software to implement the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

BAYESIAN DATA AUGMENTATION DOSE FINDING WITH CONTINUAL REASSESSMENT METHOD AND DELAYED TOXICITY

A major practical impediment when implementing adaptive dose-finding designs is that the toxicity outcome used by the decision rules may not be observed shortly after the initiation of the treatment. To address this issue, we propose the data augmentation continual re-assessment method (DA-CRM) for dose finding. By naturally treating the unobserved toxicities as missing data, we show that such missing data are nonignorable in the sense that the missingness depends on the unobserved outcomes. The Bayesian data augmentation approach is used to sample both the missing data and model parameters from their posterior full conditional distributions. We evaluate the performance of the DA-CRM through extensive simulation studies, and also compare it with other existing methods. The results show that the proposed design satisfactorily resolves the issues related to late-onset toxicities and possesses desirable operating characteristics: treating patients more safely, and also selecting the maximum tolerated dose with a higher probability. The new DA-CRM is illustrated with two phase I cancer clinical trials.

Performance of two-stage continual reassessment method relative to an optimal benchmark

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.

An adaptive model switching approach for phase I dose-finding trials

Model-based phase I dose-finding designs rely on a single model throughout the study for estimating the maximum tolerated dose (MTD). Thus, one major concern is about the choice of the most suitable model to be used. This is important because the dose allocation process and the MTD estimation depend on whether or not the model is reliable, or whether or not it gives a better fit to toxicity data. The aim of our work was to propose a method that would remove the need for a model choice prior to the trial onset and then allow it sequentially at each patient's inclusion. In this paper, we described model checking approach based on the posterior predictive check and model comparison approach based on the deviance information criterion, in order to identify a more reliable or better model during the course of a trial and to support clinical decision making. Further, we presented two model switching designs for a phase I cancer trial that were based on the aforementioned approaches, and performed a comparison between designs with or without model switching, through a simulation study. The results showed that the proposed designs had the advantage of decreasing certain risks, such as those of poor dose allocation and failure to find the MTD, which could occur if the model is misspecified.

Interplay of priors and skeletons in two-stage continual reassessment method

Model-based dose-finding designs such as the continual reassessment method (CRM) rely on some basic working model. In the Bayesian setting, these take the form of 'guess estimates' of the probabilities of toxicity at each level. In the likelihood setting, these estimates simply take the form of a model as operational characteristics are unaffected by arbitrary positive power transformations. These initial estimates are often referred to as the model skeleton. The impact of any prior distribution on the model parameter that describes the dose-toxicity curve will itself depend on the skeleton being used. We study the interplay between prior assumptions and skeleton choice in the context of two-stage CRM designs. We consider the behavior of a two-stage design at the point of transition from a 3 + 3 design to CRM. We study how use can be made of stage 1 data to construct a more efficient skeleton in conjunction with any prior information through an example of a clinical trial. We evaluate to what extent stage 1 data might be down weighted when the maximum tolerated dose (MTD) is far from the starting level and stage 1 data is strongly informative. The results show no improvement in accuracy; thus, weighting is not necessary unless the investigators feel strongly about the location of the MTD and wish to accelerate into the vicinity of the MTD. In general, because this information is not available, we recommend that the design of two-stage trials utilize stage 1 data to establish a skeleton.

Extended model-based designs for more complex dose-finding studies

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.