Real World Evidence Generation And Evaluation Of Therapeutics

The volume and complexity of information about individual patients is greatly increasing with use of electronic records and personal devices. Potential effects on medical product development in the context of this wealth of real-world data could be numerous and varied, ranging from the ability to determine both large-scale and patient-specific effects of treatments to the ability to assess how therapeutics affect patients' lives through measurement of lifestyle changes. In October 2016, the National Academies of Sciences, Engineering, and Medicine held a workshop to facilitate dialogue among stakeholders about the opportunities and challenges for incorporating real-world evidence into all stages in the process for the generation and evaluation of therapeutics. Participants explored unmet stakeholder needs and opportunities to generate new kinds of evidence that meet those needs. This publication summarizes the presentations and discussions from the workshop.

Real-world evidence (RWE) has been at the forefront of pharmaceutical innovations. It plays an important role in transforming drug development from a process aimed at meeting regulatory expectations to an operating model that leverages data from disparate sources to aid business, regulatory, and healthcare decision making. Despite its many benefits, there is no single book systematically covering the latest development in the field. Written specifically for pharmaceutical practitioners, Real-World Evidence in Drug Development and Evaluation, presents a wide range of RWE applications throughout the lifecycle of drug product development. With contributions from experienced researchers in the pharmaceutical industry, the book discusses at length RWE opportunities, challenges, and solutions. Features Provides the first book and a single source of information on RWE in drug development Covers a broad array of topics on outcomes- and value-based RWE assessments Demonstrates proper Bayesian application and causal inference for real-world data (RWD) Presents real-world use cases to illustrate the use of advanced analytics and statistical methods to generate insights Offers a balanced discussion of practical RWE issues at hand and technical solutions suitable for practitioners with limited data science expertise

Ten years after the sequencing of the human genome, scientists have developed genetic tests that can predict a person's response to certain drugs, estimate the risk of developing Alzheimer's disease, and make other predictions based on known links between genes and diseases. However, genetic tests have yet to become a routine part of medical care, in part because there is not enough evidence to show they help improve patients' health. The Institute of Medicine (IOM) held a workshop to explore how researchers can gather better evidence more efficiently on the clinical utility of genetic tests. Generating Evidence for Genomic Diagnostic Test Development compares the evidence that is required for decisions regarding clearance, use, and reimbursement, to the evidence that is currently generated. The report also addresses innovative and efficient ways to generate high-quality evidence, as well as barriers to generating this evidence. Generating Evidence for Genomic Diagnostic Test Development contains information that will be of great value to regulators and policymakers, payers, health-care providers, researchers, funders, and evidence-based review groups.

This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.

In the wake of publicity and congressional attention to drug safety issues, the Food and Drug Administration (FDA) requested the Institute of Medicine assess the drug safety system. The committee reported that a lack of clear regulatory authority, chronic underfunding, organizational problems, and a scarcity of post-approval data about drugs' risks and benefits have hampered the FDA's ability to evaluate and address the safety of prescription drugs after they have reached the market. Noting that resources and therefore efforts to monitor medications' riskâ€"benefit profiles taper off after approval, The Future of Drug Safety offers a broad set of recommendations to ensure that consideration of safety extends from before product approval through the entire time the product is marketed and used.

This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)

Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research--from funders, to researchers, to journals, to physicians, and ultimately, to patients.

From Research to Reality describes the stages involved in the approval and use of gene therapies in Canada, and examines challenges associated with regulatory oversight, manufacturing, access, and affordability, and identifies promising approaches to address them.