Telba Zalkind Irony

Bayesian approaches are increasingly used in drug development to optimize clinical trial design and analysis by formally integrating prior knowledge, quantifying uncertainty, and supporting decision making throughout the development lifecycle. Nevertheless, broader implementation in confirmatory settings remains constrained by perceived technical complexity and uncertainty regarding regulatory acceptance. This article presents practical applications drawn from our experience, with a focus on how Bayesian thinking can improve drug development. We start with Bayesian methods for borrowing information in clinical trials and explore applications such as external control augmentation and treatment effect extrapolation. We discuss key challenges, including demonstration of exchangeability, over‐borrowing, bias, and potential type I error inflation, and outline mitigation strategies such as dynamic borrowing and sensitivity analyses. Regulatory implications of these approaches are illustrated through two pediatric case studies, one unsuccessful and one accepted, highlighting features that influence regulatory acceptability. We then present examples showing how Bayesian models can enhance analytical decision‐making during trial conduct even when the primary analyses rely on frequentist methods. Next, we introduce Assurance and Conditional Assurance and demonstrate how these Bayesian metrics can be used to optimize development strategies, including phase progression decisions, number of registrational trials, futility rules, enrichment strategies, and alpha spending. Finally, we illustrate how Bayesian models can support trial operations by forecasting and monitoring the probability of operational success. Overall, the article shows how Bayesian approaches, even when used alongside frequentist methods, can strengthen trial design and analysis, improve efficiency, and enhance transparency across drug development.

This chapter provides examples of current use of Bayesian approaches in the regulation of medical products, focusing on the use of prior information to increase the power of clinical trials, the use of Bayesian adaptive designs, and the use of simulations to assess the operating characteristics of Bayesian designs. We discuss how to handle subjectivity associated with the use of information generated outside a clinical trial, which can be a primary concern for using prior distributions. We also discuss Bayesian adaptive designs including their advantages. Finally, we note that it has been customary to carry out simulations to assess and to control the operating characteristics of Bayesian designs, such as type I error rate, and to optimize key design features. The need for simulating trials at the design stage and the benefits and hurdles of conducting such simulations are also discussed in this chapter.

In Reply First, as stated in the JAMA Guide to Statistics and Methods article, case-control studies provide the estimate of the OR rather than the RR because th

Retrievable inferior vena cava (IVC) filters are increasingly implanted prophylactically in patients who may be at transient risk of pulmonary embolism (PE). In 2009, the Agency detected an increased trend in the number of adverse events (AEs) related to implanted IVC filters, which was considered a possible safety signal. To properly address the total product life cycle of these devices, an internal multidisciplinary working group was developed with representation from all offices involved in the review and surveillance of these devices with the intent to investigate further these findings and decide as to whether or not regulatory actions should be taken to mitigate any potential risks. Since the majority of these IVC filters are not removed after the risk of PE has diminished and the true risks of developing a device-related complication after successful placement of an IVC filter is unknown, but such risks would be expected to increase with the duration of implantation as with any chronic implant, the Agency developed a quantitative decision analysis model to assess the benefits and risks associated with these devices to elucidate whether implanted filters should be removed once the risk of PE starts to decrease (Morales et al. 2013). This chapter discusses the benefit–risk assessment that employed a quantitative decision analysis model to address this issue in the absence of well-controlled clinical studies. On the basis of literature review and expert opinion, the model weighs the risks against the benefits of retrievable IVC filter use as a function of the filter’s time in situ so that the optimal time to remove the filters can be easily identified. The analysis showed that the optimal time to remove the IVC filters from patients without PE and who may be at transient risk of PE is between 1 and 2 months after implantation. As a result of this analysis, the Center for Devices and Radiological Health at the Food and Drug Administration (FDA) issued two Public Health Notifications, one in 2010 252and another in 2012, recommending removing retrievable IVC filters as soon as protection from PE is no longer needed. Our analysis provided supportive evidence to FDA’s recommendations. At the end of this chapter, we discussed practical issues and future applications of similar quantitative models.

54In March 2012, the Center for Devices and Radiological Health at the Food and Drug Administration issued a guidance document describing the factors to be considered when the Center makes benefit–risk determinations for medical device premarket reviews. Decision analysis provided the foundations and motivation for this guidance to industry and staff who have to weigh these factors when making benefit–risk determinations for medical device approval. In this chapter, we will discuss these factors and briefly provide examples to illustrate how to weigh these factors in order to reach regulatory decisions for approval of medical devices. A groundbreaking factor described in the guidance is “patient tolerance for risk and perspective on benefit.” Until now, this factor has not been formally considered in the regulatory setting. Through an example, we will demonstrate how to obtain quantitative evidence on how patients weigh benefits against risks and how this evidence could be used in the regulatory setting.

This JAMA Guide to Statistics and Methods reviews the use of composite outcome measures in clinical trials and discusses how their use should influence interpre

The US Food and Drug Administration (FDA) understands the value of patient input in the regulatory decision‐making process and has worked to enhance meaningful engagement. In recent years, there has been an increased scientific demand for more systematic and quantitative approaches to incorporate patient input throughout the medical product lifecycle, including to inform regulatory benefit‐risk assessments. The use of patient preference information (PPI), elicited using established scientific methods, is a promising strategy for accomplishing this.Although much of the science behind PPI is not new, its application in a regulatory setting will require adapting and advancing the science of identifying, collecting, and evaluating patient input for informing regulatory decision making. Patient input and empowerment are foundational to a learning healthcare system. A learning healthcare system paradigm can also help us better understand and continuously improve the incorporation of the patient perspective in regulatory decision making.In this article, we highlight the Food and Drug Administration's Center for Biologics Evaluation and Research experience and current initiatives on advancing the science of patient input in a regulatory setting, in particular, PPI. We provide a use case that explores how the principles and benefits of PPI applied in shared clinical decision making can be realized and leveraged to enhance regulatory evaluation of innovative therapies. To further advance the application of the science of patient input in our regulatory framework, we compiled a list of example resources that support stakeholders in designing and conducting PPI studies. More collaborative research among stakeholders is needed to establish best practice approaches, ensure scientific validity, and continuously learn and improve the systematic incorporation of scientific patient input throughout the regulatory decision‐making process.