Keaven M. Anderson

Good Practices for Adaptive Clinical Trials in Pharmaceutical Product Development

This article is a summary of good adaptive practices for the planning and implementation of adaptive designs compiled from experiences gained in the pharmaceutical industry. The target audience is anyone involved in the planning and execution of clinical trials. The first step prior to planning an adaptive design is to assess the appropriateness of its use. Hence, strategic points to consider when assessing if an adaptive design is the right choice for a trial are discussed. In addition, strategic points for consideration at the design and implementation stage are included from operational, regulatory, clinical, and statistical perspectives. Good practices for trial simulation, trial documentation, and data monitoring committees are provided.

Sample Size Reestimation: A Review and Recommendations:

Despite best efforts, some crucial information used to design a confirmatory trial may not be available, or may be available but with a high degree of uncertainty, at the design stage. When this happens, it may be prudent to check the validity of those assumptions using interim data from the study and make midcourse adjustment if necessary. One such adjustment is to modify the sample size. In this article, we focus on sample size reestimation (SSR) for phase III and IV studies. The discussion is relevant to both continuous and binary endpoints even though the basis for SSR might differ for those two cases. We review commonly used approaches to adjust sample size and provide recommendations on how SSR should be implemented to achieve the objectives and maintain the integrity of the trial. The recommendations cover scientific, procedural, and logistic considerations.

On Adaptive Extensions of Group Sequential Trials for Clinical Investigations

In group sequential trials, it is important to obtain adequate data to assess overall benefits and risks of an experimental treatment for patients. To achieve this goal, we provide a general, formal framework for adaptively extending a group sequential trial to stop at any interim analysis time, often after a significance boundary for a clinical endpoint is crossed. For statistical inference, we propose to order the sample space by a class of well-ordered group sequential tests. On this basis, we develop a unified sequential statistical inference approach that is applicable to both interim monitoring and final analysis. We also show that the new ordering provides the foundation for the repeated confidence intervals procedure of Jennison and Turnbull.

The BATTLE Trial: A Bold Step toward Improving the Efficiency of Biomarker-Based Drug Development

Successful completion of the Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial, reported in this issue of Cancer Discovery, is an important advance in the effort to improve clinical trial approaches to the simultaneous development of new therapeutics with matching diagnostic tests so that patients most likely to benefit from these therapies can be identified.

Viewpoints on the FDA Draft Adaptive Designs Guidance from the PhRMA Working Group

The US Food and Drug Administration has recently released a draft guidance document on adaptive clinical trials. We comment on the document from the particular perspective of the authors as members of a PhRMA working group on this topic, which has interacted with FDA personnel on adaptive trial issue during recent years. We describe the activities and prior work of our working group, and use this as a basis to discuss the content of the guidance document as it relates to several issues of current relevance, such as data monitoring processes, adaptive dose finding, so-called seamless trial designs, and sample size reestimation.

Practical Considerations and Strategies for Executing Adaptive Clinical Trials

There is great potential for clinical trials designed with adaptive features to result in more efficient decision making within a drug development program. However, clinical trials with adaptive features are more complex to implement than traditional designs such as fixed-sample or group sequential. Workarounds and/or inefficiencies in adaptive design (AD) trial execution may result in human and material wastes. Further, they may result in the introduction of operational biases that may potentially negate any gains in designing an AD trial and may even render trial results not interpretable. In this article, we present and share our experience and best practices in AD trial implementation in the areas of resource planning, randomization considerations including the importance of randomization schemes on clinical supply, Interactive Voice Randomization System vendor capability assessment and quality control, clinical supply strategy considerations, enrollment management and patient enrollment modeling and simulation, data quality and interim analysis planning, managing blinding and unblinding, and the use of a data monitoring committee.

Fitting spending functions

Group sequential monitoring is used to provide guidance on stopping a clinical trial in progress based on interim evaluation of its efficacy objectives. A trial could stop because an experimental regimen (1) is efficacious, (2) lacks any sign of efficacy, or (3) is specifically less efficacious than a control. Group sequential methods using alpha- and beta-spending functions (Biometrika 1983; 70:659-663) are often used to create stopping boundaries for test statistics for efficacy hypotheses computed at interim analyses. This paper explores fitting alpha- and beta-spending functions that have specific values at specific interim analyses. Commonly used one-parameter families may not provide an adequate fit to more than one desired critical value. We define new one- and two-parameter families to provide additional flexibility along with examples to demonstrate their usefulness. The logistic family is one of these two-parameter families, which has been applied in several trials.

A 2-in-1 adaptive phase 2/3 design for expedited oncology drug development☆

We propose an adaptive design that allows us to expand an ongoing Phase 2 trial into a Phase 3 trial to expedite a drug development program with fewer patients. Rather than the usual practice of increasing sample size with a less positive interim outcome, here we propose maintaining sample size with such a result and wait for fully mature data. The final Phase 2 data may be negative, may warrant a larger Phase 3 trial, or, in the extreme, could provide a definitively positive outcome. If the interim outcome is more positive, the trial continues to an originally planned larger sample size for a definitive Phase 3 evaluation. All patients from the study are used for inference regardless of the interim expansion decision. We show that no penalty needs to be paid in order to control the overall Type I error of the study, under a mild assumption that is expected to generally hold in practice. The proposed design may be considered an alternative approach to sample size adjustment for ongoing trials. As such, the use of an intermediate endpoint for adaptive decision is a unique feature of the design. A hypothetical example is provided for illustration purpose.

Baseline Tumor Size Is an Independent Prognostic Factor for Overall Survival in Patients with Melanoma Treated with Pembrolizumab

Purpose: The purpose of this study was to assess the association of baseline tumor size (BTS) with other baseline clinical factors and outcomes in pembrolizumab-treated patients with advanced melanoma in KEYNOTE-001 (NCT01295827). Experimental Design: BTS was quantified by adding the sum of the longest dimensions of all measurable baseline target lesions. BTS as a dichotomous and continuous variable was evaluated with other baseline factors using logistic regression for objective response rate (ORR) and Cox regression for overall survival (OS). Nominal P values with no multiplicity adjustment describe the strength of observed associations. Results: Per central review by RECIST v1.1, 583 of 655 patients had baseline measurable disease and were included in this post hoc analysis. Median BTS was 10.2 cm (range, 1–89.5). Larger median BTS was associated with Eastern Cooperative Oncology Group performance status 1, elevated lactate dehydrogenase (LDH), stage M1c disease, and liver metastases (with or without any other sites; all P ≤ 0.001). In univariate analyses, BTS below the median was associated with higher ORR (44% vs. 23%; P < 0.001) and improved OS (HR, 0.38; P < 0.001). In multivariate analyses, BTS below the median remained an independent prognostic marker of OS (P < 0.001) but not ORR. In 459 patients with available tumor programmed death ligand 1 (PD-L1) expression, BTS below the median and PD-L1–positive tumors were independently associated with higher ORR and longer OS. Conclusions: BTS is associated with many other baseline clinical factors but is also independently prognostic of survival in pembrolizumab-treated patients with advanced melanoma. Clin Cancer Res; 24(20); 4960–7. ©2018 AACR. See related commentary by Warner and Postow, p. 4915

On Efficient Two-Stage Adaptive Designs for Clinical Trials with Sample Size Adjustment

Group sequential designs are rarely used for clinical trials with substantial over running due to fast enrollment or long duration of treatment and follow-up. Traditionally, such trials rely on fixed sample size designs. Recently, various two-stage adaptive designs have been introduced to allow sample size adjustment to increase statistical power or avoid unnecessarily large trials. However, these adaptive designs can be seriously inefficient. To address this infamous problem, we propose a likelihood-based two-stage adaptive design where sample size adjustment is derived from a pseudo group sequential design using cumulative conditional power. We show through numerical examples that this design cannot be improved by group sequential designs. In addition, the approach may uniformly improve any existing two-stage adaptive designs with sample size adjustment. For statistical inference, we provide methods for sequential p-values and confidence intervals, as well as median unbiased and minimum variance unbiased estimates. We show that the claim of inefficiency of adaptive designs by Tsiatis and Mehta (2003) is logically flawed, and thereby provide a strong defense of Cui et al. (1999).