Meta-Analyses and Adaptive Group Sequential Designs in the Clinical Development Process

A Bayesian adaptive design approach for stepped-wedge cluster randomized trials

BACKGROUND

The Bayesian group sequential design has been applied widely in clinical studies, especially in Phase II and III studies. It allows early termination based on accumulating interim data. However, to date, there lacks development in its application to stepped-wedge cluster randomized trials, which are gaining popularity in pragmatic trials conducted by clinical and health care delivery researchers.

METHODS

We propose a Bayesian adaptive design approach for stepped-wedge cluster randomized trials, which makes adaptive decisions based on the predictive probability of declaring the intervention effective at the end of study given interim data. The Bayesian models and the algorithms for posterior inference and trial conduct are presented.

RESULTS

We present how to determine design parameters through extensive simulations to achieve desired operational characteristics. We further evaluate how various design factors, such as the number of steps, cluster size, random variability in cluster size, and correlation structures, impact trial properties, including power, type I error, and the probability of early stopping. An application example is presented.

CONCLUSION

This study presents the incorporation of Bayesian adaptive strategies into stepped-wedge cluster randomized trials design. The proposed approach provides the flexibility to stop the trial early if substantial evidence of efficacy or futility is observed, improving the flexibility and efficiency of stepped-wedge cluster randomized trials.

Adaptive design implementation in confirmatory trials: methods, practical considerations and case studies

The rapidly changing drug development landscapes have brought unique challenges to sponsors in designing clinical trials in a faster and more efficient way. With the ability to accelerate development timeline, reduce redundant sample size, and select the right dose and patient population during the clinical trial, adaptive designs help to increase the probability of success of clinical trials and eventually contribute to bringing the promising drugs to patients earlier and fulfilling their unmet medical needs. Although extensive adaptive design methods have been proposed in recent years, a comprehensive review of how to implement adaptive design in the practical confirmatory trials is still lacking. In this paper, we will review the evolving history of adaptive designs, updates of newly released regulatory guidance and emerging practical adaptive designs, including but not limited to sample size re-estimation, seamless design and surrogate endpoint used in the interim analysis. Furthermore, we will discuss the current practice of adaptive design implementation by demonstrating a complex oncology seamless phase 2/3 adaptive design case study. Through this example, we will introduce the critical roles of each cross disciplinary function, communication process and important documents when adaptive designs are implemented in real-world setting.

Adaptive designs from a Data Safety Monitoring Board perspective: Some controversies and some case studies

This article describes vignettes concerning interactions with Data Safety Monitoring Boards during the design and monitoring of some clinical trials with an adaptive design. Most reflect personal experiences by the author.

Adaptive design clinical trials: Methodology, challenges and prospect

New drug development is a time-consuming and expensive process. Recently, there has been stagnation in the development of novel compounds. Moreover, the attrition rate in clinical research is also on the rise. Fearing more stagnation, the Food and Drug Administration released the critical path initiative in 2004 and critical path opportunity list in 2006 thus highlighting the need of advancing innovative trial designs. One of the innovations suggested was the adaptive designed clinical trials, a method promoting introduction of pre-specified modifications in the design or statistical procedures of an on-going trial depending on the data generated from the concerned trial thus making a trial more flexible. The adaptive design trials are proposed to boost clinical research by cutting on the cost and time factor. Although the concept of adaptive designed clinical trials is round-the-corner for the last 40 years, there is still lack of uniformity and understanding on this issue. This review highlights important adaptive designed methodologies besides covering the regulatory positions on this issue.

Bayesian statistics in oncology: a guide for the clinical investigator

The rise of evidence-based medicine as well as important progress in statistical methods and computational power have led to a second birth of the >200-year-old Bayesian framework. The use of Bayesian techniques, in particular in the design and interpretation of clinical trials, offers several substantial advantages over the classical statistical approach. First, in contrast to classical statistics, Bayesian analysis allows a direct statement regarding the probability that a treatment was beneficial. Second, Bayesian statistics allow the researcher to incorporate any prior information in the analysis of the experimental results. Third, Bayesian methods can efficiently handle complex statistical models, which are suited for advanced clinical trial designs. Finally, Bayesian statistics encourage a thorough consideration and presentation of the assumptions underlying an analysis, which enables the reader to fully appraise the authors' conclusions. Both Bayesian and classical statistics have their respective strengths and limitations and should be viewed as being complementary to each other; we do not attempt to make a head-to-head comparison, as this is beyond the scope of the present review. Rather, the objective of the present article is to provide a nonmathematical, reader-friendly overview of the current practice of Bayesian statistics coupled with numerous intuitive examples from the field of oncology. It is hoped that this educational review will be a useful resource to the oncologist and result in a better understanding of the scope, strengths, and limitations of the Bayesian approach.

Group sequential and adaptive designs - a review of basic concepts and points of discussion

In recent times, group sequential and adaptive designs for clinical trials have attracted great attention from industry, academia and regulatory authorities. These designs allow analyses on accumulating data - as opposed to classical, "fixed-sample" statistics. The rapid development of a great variety of statistical procedures is accompanied by a lively debate on their potential merits and shortcomings. The purpose of this review article is to ease orientation in both respects. First, we provide a concise overview of the essential technical concepts, with special emphasis on their interrelationships. Second, we give a structured review of the current controversial discussion on practical issues, opportunities and challenges of these new designs.

Adaptive design methods in clinical trials - a review

In recent years, the use of adaptive design methods in clinical research and development based on accrued data has become very popular due to its flexibility and efficiency. Based on adaptations applied, adaptive designs can be classified into three categories: prospective, concurrent (ad hoc), and retrospective adaptive designs. An adaptive design allows modifications made to trial and/or statistical procedures of ongoing clinical trials. However, it is a concern that the actual patient population after the adaptations could deviate from the originally target patient population and consequently the overall type I error (to erroneously claim efficacy for an infective drug) rate may not be controlled. In addition, major adaptations of trial and/or statistical procedures of on-going trials may result in a totally different trial that is unable to address the scientific/medical questions the trial intends to answer. In this article, several commonly considered adaptive designs in clinical trials are reviewed. Impacts of ad hoc adaptations (protocol amendments), challenges in by design (prospective) adaptations, and obstacles of retrospective adaptations are described. Strategies for the use of adaptive design in clinical development of rare diseases are discussed. Some examples concerning the development of Velcade intended for multiple myeloma and non-Hodgkin's lymphoma are given. Practical issues that are commonly encountered when implementing adaptive design methods in clinical trials are also discussed.

A composite design for transition from a preliminary to a full-scale study

In drug development, a sequence of studies are conducted to evaluate effectiveness (or efficacy) and safety, such as a Phase II study to assess pharmacological activity or safety that is then followed by a definitive Phase III study to assess clinical effectiveness. Rather than conducting separate successive studies, we describe a design in which the patients enrolled in a preliminary (e.g. Phase II) study are continued into a subsequent full-scale (e.g. Phase III) study. This design also applies to a study that uses an internal pilot with a preliminary assessment of efficacy or safety. The combined preliminary to full-scale design potentially reduces the total numbers of patients required and the total duration of the program. The design allows a futility or safety stopping boundary for the preliminary study result that is specified in terms of a lower critical Z-value (Z(L)) and the pursuant type II error probability incurred under a specified alternative hypothesis of a beneficial effect or no toxicity at that stage. This boundary also leads to a deflation of the type I error probability for the final test at the completion of the full-scale study, such that a critical value for the final test (Z(F)) can be determined that provides the desired level of the type I error probability exactly. Thus, it is possible to determine sample sizes at the two stages, and critical values Z(L) and Z(F) that provide specified type I and II error probabilities for the combined study. We describe the design using large-sample normally distributed Z-tests at the two phases, including a test for means, or proportions or survival times, or combinations thereof, such as a test for means at Phase II followed by a test for proportions at Phase III. We compare the properties of this design versus the conduct of two successive studies, and explore the factors that influence the operating characteristics of the design. We also discuss the practical considerations in the implementation of the design.

An Adaptive Two-stage Design with Treatment Selection Using the Conditional Error Function Approach

As an approach to combining the phase II dose finding trial and phase III pivotal trials, we propose a two-stage adaptive design that selects the best among several treatments in the first stage and tests significance of the selected treatment in the second stage. The approach controls the type I error defined as the probability of selecting a treatment and claiming its significance when the selected treatment is indifferent from placebo, as considered in Bischoff and Miller (2005). Our approach uses the conditional error function and allows determining the conditional type I error function for the second stage based on information observed at the first stage in a similar way to that for an ordinary adaptive design without treatment selection. We examine properties such as expected sample size and stage-2 power of this design with a given type I error and a maximum stage-2 sample size under different hypothesis configurations. We also propose a method to find the optimal conditional error function of a simple parametric form to improve the performance of the design and have derived optimal designs under some hypothesis configurations. Application of this approach is illustrated by a hypothetical example.