Adaptive Informational Design of Confirmatory Phase III Trials With an Uncertain Biomarker Effect to Improve the Probability of Success

Tests and classification methods in adaptive designs with applications

Statistical tests for biomarker identification and classification methods for patient grouping are two important topics in adaptive designs of clinical trials related to genomic studies. In this article, we evaluate four test methods for biomarker identification in the first stage of an adaptive design: a model-based identification method, the popular two-sided t-test, the nonparametric Wilcoxon Rank-Sum test (two-sided), and the Regularized Generalized Linear Models. For patients grouping in the second stage, we examine classification methods such as Random Forest, Elastic-net Regularized Generalized Linear Models, Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). Simulation studies are carried out to assess the performance of the different methods. The best identification methods are chosen based on the well-known F 1 score, while the best classification techniques are selected based on the area under a receiver operating characteristic curve (AUC). The chosen methods are then applied to the Adaptive Signature Design (ASD) with a real data set from breast cancer patients for the purpose of evaluating the performance of ASD in different situations.

The optimal design of clinical trials with potential biomarker effects: A novel computational approach

As a future trend of healthcare, personalized medicine tailors medical treatments to individual patients. It requires to identify a subset of patients with the best response to treatment. The subset can be defined by a biomarker (eg, expression of a gene) and its cutoff value. Topics on subset identification have received massive attention. There are over two million hits by keyword searches on Google Scholar. However, designing clinical trials that utilize the discovered uncertain subsets/biomarkers is not trivial and rarely discussed in the literature. This leads to a gap between research results and real-world drug development. To fill in this gap, we formulate the problem of clinical trial design into an optimization problem involving high-dimensional integration, and propose a novel computational solution based on Monte Carlo and smoothing methods. Our method utilizes the modern techniques of general purpose computing on graphics processing units for large-scale parallel computing. Compared to a published method in three-dimensional problems, our approach is more accurate and 133 times faster. This advantage increases when dimensionality increases. Our method is scalable to higher dimensional problems since the precision bound of our estimated study power is a finite number not affected by dimensionality. To design clinical trials incorporating the potential biomarkers, users can use our software "DesignCTPB". This software can be found on Github and will be available as an R package on CRAN. Although our research is motivated by the design of clinical trials, the method can be used widely to solve other optimization problems involving high-dimensional integration.

Clinical trial risk in leukemia: Biomarkers and trial design

This study analyzed the risk of clinical trial failure for leukemia drug development between January 1999 and January 2020. The specific leukemia subtypes of interest were acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), and chronic myeloid leukemia (CML). Drug development was investigated using data obtained from https://www.clinicaltrials.gov and other publicly available databases. Drug compounds were excluded if they began phase I testing for the indication of interest before January 1999, if they were not industry sponsored, or if they treated secondary complications of the disease. Further analysis was conducted on biomarker usage, drug mechanisms of action, and line of treatment. Drugs were identified following our inclusion criteria for ALL (72), CLL (106), AML (159), and CML (47). The likelihood (cumulative pass rate), a drug would pass all phases of clinical testing and obtain Food and Drug Administration approval, was 18% (ALL), 10% (CLL), 7% (AML), and 12% (CML). Biomarker targeted therapies improved the success rates by three- and sevenfold, for ALL and AML, respectively. Enzyme inhibitors doubled the cumulative success rate for AML. First-line therapy and kinase inhibitors both independently doubled the cumulative success rate for CLL. Oncologists enrolling patients in clinical trials can increase success rates by up to sevenfold by prioritizing participation in trials involving biomarker usage, while consideration of factors such as drug mechanism of action and line of therapy can further double the clinical trial success rate.

MASTER KEY Project: Powering Clinical Development for Rare Cancers Through a Platform Trial

For rare cancers, challenges in establishing standard therapies are greater than those for major cancers, and effective methods are needed. MASTER KEY Project is a multicenter study based in Japan, with two main parts: prospective registry study and multiple clinical trials. Advanced rare cancers, cancers of unknown primary origin, and those with rare tissue subtypes of common cancers are targeted. The registry study accumulates highly reliable consecutive data that can be used for future drug development. The multiple trials are conducted simultaneously, targeting either a specific biomarker or a rare tumor type of interest. The first interim data set from the registry part presented here shows the prevalence of genetic abnormalities, response rates, survival rates, and clinical trial enrollment rates. From May 2017 to April 2019, 560 patients (mean age = 53) were enrolled in the project. Frequent cancer types included soft tissue sarcomas, neuroendocrine tumors, and central nervous system tumors. Among the 528 patients with assessable data, 69% (364/528) had next‐generation sequencing tests, with 48% (176/364) harboring an “actionable” alteration. Seventy‐one (13%) patients have been enrolled in one of the clinical trials, with an accrual rate of 3.94 patients/month. A descriptive analysis of biomarker‐directed or non‐biomarker‐directed treatment survival was performed. This project is expected to accelerate development of treatments for rare cancers and show that comprehensive platform trials are an advantageous strategy.

A roadmap to using historical controls in clinical trials - by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG)

Historical controls (HCs) can be used for model parameter estimation at the study design phase, adaptation within a study, or supplementation or replacement of a control arm. Currently on the latter, there is no practical roadmap from design to analysis of a clinical trial to address selection and inclusion of HCs, while maintaining scientific validity. This paper provides a comprehensive roadmap for planning, conducting, analyzing and reporting of studies using HCs, mainly when a randomized clinical trial is not possible. We review recent applications of HC in clinical trials, in which either predominantly a large treatment effect overcame concerns about bias, or the trial targeted a life-threatening disease with no treatment options. In contrast, we address how the evidentiary standard of a trial can be strengthened with optimized study designs and analysis strategies, emphasizing rare and pediatric indications. We highlight the importance of simulation and sensitivity analyses for estimating the range of uncertainties in the estimation of treatment effect when traditional randomization is not possible. Overall, the paper provides a roadmap for using HCs.

Empowering phase II clinical trials to reduce phase III failures

The large number of failures in phase III clinical trials, which occur at a rate of approximately 45%, is studied herein relative to possible countermeasures. First, the phenomenon of failures is numerically described. Second, the main reasons for failures are reported, together with some generic improvements suggested in the related literature. This study shows how statistics explain, but do not justify, the high failure rate observed. The rate of failures due to a lack of efficacy that are not expected, is considered to be at least 10%. Expanding phase II is the simplest and most intuitive way to reduce phase III failures since it can reduce phase III false negative findings and launches of phase III trials when the treatment is positive but suboptimal. Moreover, phase II enlargement is discussed using an economic profile. As resources for research are often limited, enlarging phase II should be evaluated on a case-by-case basis. Alternative strategies, such as biomarker-based enrichments and adaptive designs, may aid in reducing failures. However, these strategies also have very low application rates with little likelihood of rapid growth.

Advancing cancer drug development through precision medicine and innovative designs

Precision medicine has been a hot topic in drug development over the last decade. Biomarkers have been proven useful for understanding the disease progression and treatment response in precision medicine development. Advancement of high-throughput omics technologies has enabled fast identification of molecular biomarkers with low cost. Although biomarkers have brought many promises to drug development, steep challenges arise due to a large amount of data, complexity of technology, and lack of full understanding of biology. In this article, we discuss the technologies and statistical issues that are related to omics biomarker discovery. We also provide an overview of the current development of biomarker-enabled cancer clinical trial designs.

Timing of the interim analysis in adaptive enrichment designs

With increasing interest in personalized medicine over the last years, study designs allowing to demonstrate efficacy in particular subgroups of the overall patient population become more important. Adaptive enrichment designs provide the possibility to both selecting the target population with the most promising treatment benefit and testing for efficacy within a single trial. Here, the target population is selected in a prespecified interim analysis. So far, it has not been very well investigated how timing of the interim analysis should be chosen. We investigate the impact of the interim analysis timing on power for the situation of a normally distributed outcome considering two different classes of selection rules. The interim selection is based either on the estimated effect difference between subgroup and total population or on absolute effect estimates. In this article, we demonstrate that there are indeed scenarios in which the timing of the interim analysis has a large impact on power. However, no universally applicable timing with favorable performance exist since power depends on treatment effects, subgroup prevalence, and especially the applied selection rule. Instead, the operating characteristics should be investigated for the specific scenario at hand to determine the most appropriate timing.

Estimation of treatment effect in two-stage confirmatory oncology trials of personalized medicines

A personalized medicine may benefit a subpopulation with certain predictive biomarker signatures or certain disease types. However, there is great uncertainty about drug activity in a subpopulation when designing a confirmatory trial in practice, and it is logical to take a two-stage approach with the study unless credible external information is available for decision-making purpose. The first stage deselects (or prunes) non-performing subpopulations at an interim analysis, and the second stage pools the remaining subpopulations in the final analysis. The endpoints used at the two stages can be different in general. A key issue of interest is the statistical property of the test statistics and point estimate at the final analysis. Previous research has focused on type I error control and power calculation for such two-stage designs. This manuscript will investigate estimation bias of the treatment effect, which is implicit in the adjustment of nominal type I error for multiplicity control in such two-stage designs. Previous work handles the treatment effect of an intermediate endpoint as a nuisance parameter to provide the most conservative type I error control. This manuscript takes the same approach to explore the bias. The methodology is applied to the two previously studied designs. In the first design, patients with different biomarker levels are enrolled in a study, and the treatment effect is assumed to be in an order. The goal of the interim analysis is to identify a biomarker cut-off point for the subpopulations. In the second design, patients with different tumour types but the same biomarker signature are included in a trial applying a basket design. The goal of the interim analysis is to identify a subset of tumour types in the absence of treatment effect ordering. Closed-form equations are provided for the estimation bias as well as the variance under the two designs. Simulations are conducted under various scenarios to validate the analytic results that demonstrated that the bias can be properly estimated in practice. Worked examples are presented. Extensions to general adaptive designs and operational considerations are discussed. Copyright © 2017 John Wiley & Sons, Ltd.