Christy Chuang-Stein

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.

In March 2011 ——— (2011), Code of Federal Regulations Title 21 Food and Drugs Chapter I Food and Drug Administration Department of Health and Human Services Subchapter D Drugs for Human Use Part 312 Investigational New Drug Application. [Google Scholar] , a Final Rule for expedited reporting of serious adverse events took effect in the United States for studies conducted under an Investigational New Drug (IND) application. In December 2012, the U.S. Food and Drug Administration (FDA) promulgated a final Guidance describing the operationalization of this Final Rule. The Rule and Guidance clarified that a clinical trial sponsor should have evidence suggesting causality before defining an unexpected serious adverse event as a suspected adverse reaction that would require expedited reporting to the FDA. The Rule's emphasis on the need for evidence suggestive of a causal relation should lead to fewer events being reported but, among those reported, a higher percentage actually being caused by the product being tested. This article reviews the practices that were common before the Final Rule was issued and the approach the New Rule specifies. It then discusses methods for operationalizing the Final Rule with particular focus on relevant statistical considerations. It concludes with a set of recommendations addressed to Sponsors and to the FDA in implementing the Final Rule.

The last 15 years have seen a substantial increase in efforts devoted to safety assessment by statisticians in the pharmaceutical industry. While some of these efforts were driven by regulations and public demand for safer products, much of the motivation came from the realization that there is a strong need for a systematic approach to safety planning, evaluation, and reporting at the program level throughout the drug development life cycle. An efficient process can help us identify safety signals early and afford us the opportunity to develop effective risk minimization plan early in the development cycle. This awareness has led many pharmaceutical sponsors to set up internal systems and structures to effectively conduct safety assessment at all levels (patient, study, and program). In addition to process, tools have emerged that are designed to enhance data review and pattern recognition. In this paper, we describe advancements in the practice of safety assessment during the premarketing phase of drug development. In particular, we share examples of safety assessment practice at our respective companies, some of which are based on recommendations from industry-initiated working groups on best practice in recent years.

Experience has shown us that when data are pooled from multiple studies to create an integrated summary, an analysis based on naïvely‐pooled data is vulnerable to the mischief of Simpson's Paradox. Using the proportions of patients with a target adverse event (AE) as an example, we demonstrate the Paradox's effect on both the comparison and the estimation of the proportions. While meta analytic approaches have been recommended and increasingly used for comparing safety data between treatments, reporting proportions of subjects experiencing a target AE based on data from multiple studies has received little attention. In this paper, we suggest two possible approaches to report these cumulative proportions. In addition, we urge that regulatory guidelines on reporting such proportions be established so that risks can be communicated in a scientifically defensible and balanced manner. Copyright © 2010 John Wiley & Sons, Ltd.