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 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.

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.

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.

This book shows how the vision for open access to scientific data can be more readily achieved through a staged model that research funders, policy makers, scientists, and research organizations can adopt in their practice. Drawing on her own experiences with data processing, on early findings with open scientific data at CERN (the European Organization for Nuclear Research), and from case studies of shared clinical trial data, the author updates our understanding of research data - what it is; how it dynamically evolves across different scientific disciplines and across various stages of research practice; and how it can, and indeed should, be shared at any of those stages. The result is a flexible and pragmatic path for implementing open scientific data.

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. .