This book draws a unique perspective on the regulation of access to clinical trial data as a case on research and knowledge externalities. Notwithstanding numerous potential benefits for medical research and public health, many jurisdictions have struggled to ensure access to clinical trial data, even at the level of the trial results. Pro-access policy initiatives have been strongly opposed by research-based drug companies arguing that mandatory data disclosure impedes their innovation incentives. Conventionally, access to test data has been approached from the perspective of transparency and research ethics. The book offers a complementary view and considers access to individual patient-level trial data for exploratory analysis as a matter of research and innovation policy. Such approach appears to be especially relevant in the data-driven economy where digital data constitutes a valuable economic resource. The study seeks to define how the rules of access to clinical trial data should be designed to reconcile the policy objectives of leveraging the research potential of data through secondary analysis, on the one hand, and protecting economic incentives of research-based drug companies, on the other hand. Overall, it is argued that the mainstream innovation-based justification for exclusive control over the outcomes of research and development can hardly rationalise trial sponsors’ control over primary data from trials. Instead, access to such data and its robust analysis should be prioritised.

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.

This book draws a unique perspective on the regulation of access to clinical trial data as a case on research and knowledge externalities. Notwithstanding numerous potential benefits for medical research and public health, many jurisdictions have struggled to ensure access to clinical trial data, even at the level of the trial results. Pro-access policy initiatives have been strongly opposed by research-based drug companies arguing that mandatory data disclosure impedes their innovation incentives. Conventionally, access to test data has been approached from the perspective of transparency and research ethics. The book offers a complementary view and considers access to individual patient-level trial data for exploratory analysis as a matter of research and innovation policy. Such approach appears to be especially relevant in the data-driven economy where digital data constitutes a valuable economic resource. The study seeks to define how the rules of access to clinical trial data should be designed to reconcile the policy objectives of leveraging the research potential of data through secondary analysis, on the one hand, and protecting economic incentives of research-based drug companies, on the other hand. Overall, it is argued that the mainstream innovation-based justification for exclusive control over the outcomes of research and development can hardly rationalise trial sponsors' control over primary data from trials. Instead, access to such data and its robust analysis should be prioritised.

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

Sharing data generated through the conduct of clinical trials offers the promise of placing evidence about the safety and efficacy of therapies and clinical interventions on a firmer basis and enhancing the benefits of clinical trials. Ultimately, such data sharing - if carried out appropriately - could lead to improved clinical care and greater public trust in clinical research and health care. Discussion Framework for Clinical Trial Data Sharing: Guiding Principles, Elements, and Activities is part of a study of how data from clinical trials might best be shared. This document is designed as a framework for discussion and public comment. This framework is being released to stimulate reactions and comments from stakeholders and the public. The framework summarizes the committee's initial thoughts on guiding principles that underpin responsible sharing of clinical trial data, defines key elements of clinical trial data and data sharing, and describes a selected set of clinical trial data sharing activities.

Part I Setting the scene -- Introduction: Individual rights, the public interest and biobank research 4000 (8) -- Genetic data and privacy protection -- Part II GDPR and European responses -- Biobank governance and the impact of the GDPR on the regulation of biobank research -- Controller' and processor's responsibilities in biobank research under GDPR -- Individual rights in biobank research under GDPR -- Safeguards and derogations relating to processing for archiving purposes in the scientific purposes: Article 89 analysis for biobank research -- A Pan-European analysis of Article 89 implementation and national biobank research regulations -- EEA, Switzerland analysis of GDPR requirements and national biobank research regulations -- Part III National insights in biobank regulatory frameworks -- Selected 10-15 countries for reports: Germany -- Greece -- France -- Finland -- Sweden -- United Kingdom -- Part IV Conclusions -- Reflections on individual rights, the public interest and biobank research, ramifications and ways forward. .

A Practical Guide to Managing Clinical Trials is a basic, comprehensive guide to conducting clinical trials. Designed for individuals working in research site operations, this user-friendly reference guides the reader through each step of the clinical trial process from site selection, to site set-up, subject recruitment, study visits, and to study close-out. Topics include staff roles/responsibilities/training, budget and contract review and management, subject study visits, data and document management, event reporting, research ethics, audits and inspections, consent processes, IRB, FDA regulations, and good clinical practices. Each chapter concludes with a review of key points and knowledge application. Unique to this book is "A View from India," a chapter-by-chapter comparison of clinical trial practices in India versus the U.S. Throughout the book and in Chapter 10, readers will glimpse some of the challenges and opportunities in the emerging and growing market of Indian clinical trials.

This handbook challenges the conventional wisdom that intellectual property is the law of creativity. Traditionally, IP has been instrumental for protecting creations of the mind, with only inventors of original works enjoying exclusive rights. Related, sui generis, and quasi-IP rights, which protect monetary investments and efforts rather than originality and inventiveness, were considered exceptions to the general principles of IP. But increasingly, IP rights are being granted to safeguard corporate investments. This handbook brings together an international roster of contributors to explore this emerging trend. Why are investments the primary driver of legal protection, and often the main requirement to obtain it? Who benefits from such new forms of protection? What should the scope of these new rights be? And are they desirable in the first place? In doing so, the volume is the first to highlight and systematically critique the move from 'intellectual' to 'investment' property.

In recent decades, advances in biomedical research have helped save or lengthen the lives of children around the world. With improved therapies, child and adolescent mortality rates have decreased significantly in the last half century. Despite these advances, pediatricians and others argue that children have not shared equally with adults in biomedical advances. Even though we want children to benefit from the dramatic and accelerating rate of progress in medical care that has been fueled by scientific research, we do not want to place children at risk of being harmed by participating in clinical studies. Ethical Conduct of Clinical Research Involving Children considers the necessities and challenges of this type of research and reviews the ethical and legal standards for conducting it. It also considers problems with the interpretation and application of these standards and conduct, concluding that while children should not be excluded from potentially beneficial clinical studies, some research that is ethically permissible for adults is not acceptable for children, who usually do not have the legal capacity or maturity to make informed decisions about research participation. The book looks at the need for appropriate pediatric expertise at all stages of the design, review, and conduct of a research project to effectively implement policies to protect children. It argues persuasively that a robust system for protecting human research participants in general is a necessary foundation for protecting child research participants in particular.

Updated as of August 2014, this practical book will demonstrate proven methods for anonymizing health data to help your organization share meaningful datasets, without exposing patient identity. Leading experts Khaled El Emam and Luk Arbuckle walk you through a risk-based methodology, using case studies from their efforts to de-identify hundreds of datasets. Clinical data is valuable for research and other types of analytics, but making it anonymous without compromising data quality is tricky. This book demonstrates techniques for handling different data types, based on the authors’ experiences with a maternal-child registry, inpatient discharge abstracts, health insurance claims, electronic medical record databases, and the World Trade Center disaster registry, among others. Understand different methods for working with cross-sectional and longitudinal datasets Assess the risk of adversaries who attempt to re-identify patients in anonymized datasets Reduce the size and complexity of massive datasets without losing key information or jeopardizing privacy Use methods to anonymize unstructured free-form text data Minimize the risks inherent in geospatial data, without omitting critical location-based health information Look at ways to anonymize coding information in health data Learn the challenge of anonymously linking related datasets