pocrm: an R-package for phase I trials of combinations of agents

A benchmark for dose-finding studies with unknown ordering

An important tool to evaluate the performance of a dose-finding design is the nonparametric optimal benchmark that provides an upper bound on the performance of a design under a given scenario. A fundamental assumption of the benchmark is that the investigator can arrange doses in a monotonically increasing toxicity order. While the benchmark can be still applied to combination studies in which not all dose combinations can be ordered, it does not account for the uncertainty in the ordering. In this article, we propose a generalization of the benchmark that accounts for this uncertainty and, as a result, provides a sharper upper bound on the performance. The benchmark assesses how probable the occurrence of each ordering is, given the complete information about each patient. The proposed approach can be applied to trials with an arbitrary number of endpoints with discrete or continuous distributions. We illustrate the utility of the benchmark using recently proposed dose-finding designs for Phase I combination trials with a binary toxicity endpoint and Phase I/II combination trials with binary toxicity and continuous efficacy.

A surface-free design for phase I dual-agent combination trials

In oncology, there is a growing number of therapies given in combination. Recently, several dose-finding designs for Phase I dose-escalation trials for combinations were proposed. The majority of novel designs use a pre-specified parametric model restricting the search of the target combination to a surface of a particular form. In this work, we propose a novel model-free design for combination studies, which is based on the assumption of monotonicity within each agent only. Specifically, we parametrise the ratios between each neighbouring combination by independent Beta distributions. As a result, the design does not require the specification of any particular parametric model or knowledge about increasing orderings of toxicity. We compare the performance of the proposed design to the model-based continual reassessment method for partial ordering and to another model-free alternative, the product of independent beta design. In an extensive simulation study, we show that the proposed design leads to comparable or better proportions of correct selections of the target combination while leading to the same or fewer average number of toxic responses in a trial.

CRM2DIM: A SAS macro for implementing the dual-agent Bayesian continual reassessment method

BACKGROUND AND OBJECTIVE

The continual reassessment method (CRM) is a model-based dose-finding design for single-agent phase I oncology trials. With the advance of targeted therapies in oncology, more and more phase I trials investigate drug combinations rather than a single agent in order to find one or more maximum tolerated dose combinations. Several designs have been proposed for such dose-finding trials but only a few software packages are available to implement them. One of the designs is the two-dimensional Bayesian CRM proposed by Wang and Ivanova. Our goal was to provide an easy-to-use program to implement this design.

METHODS

We developed a new SAS macro, CRM2DIM, for implementing this design. This macro can be used to run a phase I dose-finding trial for two-drug combination and to perform simulations.

RESULTS

We describe the program with its different features, including the possibility of running an initial design (start-up rule), the possibility of incorporating historical data, and the choice of using either a power or a logistic regression model with or without interaction term. We illustrate our program by presenting simulation results and by a hypothetical trial example.

CONCLUSIONS

The CRM2DIM macro provides a SAS implementation of the two-dimensional Bayesian CRM for dual-agent phase I oncology trials. It is an easy-to-use program that includes many useful features and provides statisticians involved in the early phases of development a new tool for designing dual-agent phase I oncology trials.

dfcomb: An R-package for phase I/II trials of drug combinations

In this paper, we present the dfcomb R package for the implementation of a single prospective clinical trial or simulation studies of phase I combination trials in oncology. The aim is to present the features of the package and to illustrate how to use it in practice though different examples. The use of combination clinical trials is growing, but the implementation of existing model-based methods is complex, so this package should promote the use of innovative adaptive designs for early phases combination trials.

Implementation of adaptive methods in early-phase clinical trials

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

A comparative study of adaptive dose-finding designs for phase I oncology trials of combination therapies

Little is known about the relative performance of competing model-based dose-finding methods for combination phase I trials. In this study, we focused on five model-based dose-finding methods that have been recently developed. We compared the recommendation rates for true maximum-tolerated dose combinations (MTDCs) and over-dose combinations among these methods under 16 scenarios for 3 × 3, 4 × 4, 2 × 4, and 3 × 5 dose combination matrices. We found that performance of the model-based dose-finding methods varied depending on (1) whether the dose combination matrix is square or not; (2) whether the true MTDCs exist within the same group along the diagonals of the dose combination matrix; and (3) the number of true MTDCs. We discuss the details of the operating characteristics and the advantages and disadvantages of the five methods compared.

Implementation of a two-group likelihood time-to-event continual reassessment method using SAS

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.

Practical designs for Phase I combination studies in oncology

Phase I trials evaluating the safety of multidrug combinations are becoming more common in oncology. Despite the emergence of novel methodology in the area, it is rare that innovative approaches are used in practice. In this article, we review three methods for Phase I combination studies that are easy to understand and straightforward to implement. We demonstrate the operating characteristics of the designs through illustration in a single trial, as well as through extensive simulation studies, with the aim of increasing the use of novel approaches in Phase I combination studies. Design specifications and software capabilities are also discussed.