Welcome to the final issue of the Biopharmaceutical (BIOP) Report for 2018! In this issue, we will review some of the productive and exciting accomplishments in 2018 for the Biopharmaceutical Section that will be continued into the following year. This issue’s featured article was written by Ilya Lipkovich of Eli Lilly and Company and Alex Dmitrienko of Mediana Inc, which describes issues and examples with Exploratory and confirmatory subgroup analysis in clinical trials.

The draft adaptive design guidance released by FDA in 2010 included references to adaptive study designs that were described as "less well-understood." At that time, there was relatively little regulatory experience with such designs, and their properties were felt to be insufficiently understood. In order to promote greater use of adaptive designs, especially those categorized as less well-understood, the Best Practice Subteam of the DIA Adaptive Designs Scientific Working Group (ADSWG) has worked on describing and characterizing these designs, identifying challenges associated with them and suggesting improvements to design or study conduct aspects that might make them more acceptable. This paper summarizes the work from the subteam.

Adaptive design (AD) clinical trials use accumulating subject data to modify the parameters of the design of an ongoing study, without compromising the validity and integrity of the study. The 2010 US Food and Drug Administration (FDA) Draft Guidance on Adaptive Design Clinical Trials described a subset of 7 primary design types as “less well-understood.” FDA defined these designs as those with limited regulatory experience. To better understand the properties of these less well-understood ADs and to promote their use when applicable, the Best Practices Subteam for DIA’s Adaptive Design Scientific Working Group conducted an extensive nonsystematic search and reviewed trials from multiple sponsors who had employed these designs. Here, we review 10 specific case studies for which less well-understood ADs were employed and share feedback about their challenges and successes, as well as details about the regulatory interactions from these trials. We learned that these designs and associated statistical methodologies can make difficult research situations more amenable for study and, therefore, are needed in our toolbox. While they can be used to study many diseases, they are particularly valuable for rare diseases, small populations, studies involving terminal illnesses, and vaccine trials, in which it is important to find efficient ways to bring effective treatments to market more rapidly. It is imperative, however, that these methodologies be utilized appropriately, which requires careful planning and precise operational execution.

This is the second article written by the American Statistical Association Biopharmaceutical Section Safety Working Group. In the first article, we reviewed the drugs approved by the United States Food and Drug Administration to treat type 2 diabetes mellitus during 2002-2014 with a focus on the cardiovascular (CV) risk assessment. We discussed different strategies to address premarketing and post-marketing CV risk requirements and raised some questions that required further research. The main objective of this article is to outline statistical challenges encountered at the design and analysis stages of cardiovascular outcome trials (CVOTs). We discuss statistical challenges and strategies for testing multiple endpoints, populations, and doses; choosing an event window; addressing premarketing and post-marketing requirements for CV events with group-sequential and adaptive designs; designing a CVOT for noninferiority and superiority testing; assessing effects in subgroups; and evaluating patient's retention and missing data challenges.

This article is a result of the efforts of the American Statistical Association Biopharmaceutical Section Working Group on Safety. With representatives from different institutions, this group reviewed the drugs approved by the United States Food and Drug Administration (FDA) to treat Type 2 diabetes mellitus during 2002-2014 with a focus on the cardiovascular (CV) risk assessment. The main objective of this article is to understand the impact of FDA guidance of 2008 on assessment of CV risk in antidiabetes development programs, which are summarized and displayed in chronological order. Compared to New Drug Applications (NDAs) submitted prior to the FDA 2008 guidance, the number of patient-years significantly increased for NDAs approved in the post-guidance era. To meet guidance requirements on CV risk assessment, meta-analyses and large cardiovascular outcome trials (CVOTs) have been conducted. These CVOTs provide an opportunity to assess safety signals beyond CV risk and assess the benefit/risk ratio better in diabetic patients with a high risk for CV events, but they also present challenges. The advantages and disadvantages of different CV assessment strategies are summarized in this manuscript. Finally, we raise some emerging questions and discuss future opportunities for CV risk assessment research. Supplementary materials for this article are available online.

Adaptive designs use accruing data to make changes in an ongoing trial according to a prespecified plan and potentially offer great efficiencies for clinical development. There are many types of adaptive designs and many trial aspects that could in theory be adapted. However, the scope of adaptive designs with relevance in confirmatory trials is narrower, and in addition, extensive pre-planning is needed and various types of challenges need to be addressed in order to use these designs in this stage of development. Nevertheless, with careful planning, there are opportunities for these designs to offer important benefits even in the confirmatory stage of development. We provide an overview of adaptive designs that have relevance for confirmatory trials and discuss considerations that may affect whether they should or should not be used in particular trials or programs as well as the challenges that need to be addressed.

In May 2012, the Committee of Health and Medicinal Products issued a concept paper on the need to review the points to consider document on multiplicity issues in clinical trials. In preparation for the release of the updated guidance document, Statisticians in the Pharmaceutical Industry held a one‐day expert group meeting in January 2013. Topics debated included multiplicity and the drug development process, the usefulness and limitations of newly developed strategies to deal with multiplicity, multiplicity issues arising from interim decisions and multiregional development, and the need for simultaneous confidence intervals (CIs) corresponding to multiple test procedures. A clear message from the meeting was that multiplicity adjustments need to be considered when the intention is to make a formal statement about efficacy or safety based on hypothesis tests. Statisticians have a key role when designing studies to assess what adjustment really means in the context of the research being conducted. More thought during the planning phase needs to be given to multiplicity adjustments for secondary endpoints given these are increasing in importance in differentiating products in the market place. No consensus was reached on the role of simultaneous CIs in the context of superiority trials. It was argued that unadjusted intervals should be employed as the primary purpose of the intervals is estimation, while the purpose of hypothesis testing is to formally establish an effect. The opposing view was that CIs should correspond to the test decision whenever possible. Copyright © 2013 John Wiley & Sons, Ltd.

Adaptive designs in clinical trials have been an evolving subject which has received much attention and research over the last decade. The first edition of this book was one of the first books devo...

Background This review discusses barriers to implementing adaptive designs in a pharmaceutical R&D environment and provides recommendations on how to overcome challenges. A summary of findings from a survey conducted through PhRMA’s working group on adaptive designs is followed by a report based on our experience as statistical and clinical consultants to project teams charged with establishing the clinical development strategy for investigational compounds and interested in applying innovative approaches. Findings and recommendations Adaptive designs require additional work in that clinical trial simulations are needed to develop the design. Some project teams, due to time and resource constraints, are unable to invest the additional effort required to conduct necessary scenario analyses of options through simulation. We recommend formally integrating the planning time for scenario analyses and to incentivize optimal designs (e.g., designs offering the highest information value per resource unit invested). Regardless of the trial design ultimately chosen, quantitatively comparing alternative trial design options through simulation will enable earlier and better decision making in the context of the overall clinical development plan. Adhering to ‘Good Adaptive Practices’ will be key to achieving this goal. Outlook Implementing adaptive designs efficiently requires top—down and bottom— up support and the willingness to invest into integrated process and information technology infrastructures. Success is conditional on the willingness of the R&D environment to embrace the implementation of adaptive designs as a Change Management Initiative in the spirit of the Critical Path of the Food and Drug Administration. Clinical Trials 2010; 7: 167—173. http://ctj.sagepub.com

AbstractThis chapter describes modeling and simulation (M&S) applied to clinical drug development within a pharmaceutical company. M&S is a tool that shows us how a drug works and helps us design and interpret clinical trials. M&S also serves as a framework for what we learn in the precise language of mathematics.

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.

There are quite a few disorders for which regulatory agencies have required a treatment to demonstrate a statistically significant effect on multiple endpoints, each at the one-sided 2.5% level, before accepting the treatment’s efficacy for the disorders. Depending on the correlation among the endpoints, this requirement could lead to a substantial reduction in the study’s power to conclude the efficacy of a treatment. To investigate the prevalence of this requirement and propose possible solutions, a multiple-disciplinary Multiple Endpoints Expert Team sponsored by Pharmaceutical Research and Manufacturers of America was formed in November 2003. The team recognized early that many researchers were not fully aware of the implications of requiring multiple co-primary endpoints. The team proposes possible solutions from both the medical and the statistical perspectives. The optimal solution is to reduce the number of multiple co-primary endpoints. If after careful considerations, multiple co-primary endpoints remain a scientific requirement, the team proposes statistical solutions and encourages that regulatory agencies be receptive to approaches that adopt modest upward adjustments of the nominal significance levels for testing individual endpoints. Finally, the team hopes that this report will draw more attention to the problem of multiple co-primary endpoints and stimulate further research.