Advertisement
| data science in clinical research: Fundamentals of Clinical Data Science Pieter Kubben, Michel Dumontier, Andre Dekker, 2018-12-21 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. |
| data science in clinical research: Sharing Clinical Trial Data Institute of Medicine, Board on Health Sciences Policy, Committee on Strategies for Responsible Sharing of Clinical Trial Data, 2015-04-20 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. |
| data science in clinical research: Clinical Research Computing Prakash Nadkarni, 2016-04-29 Clinical Research Computing: A Practitioner's Handbook deals with the nuts-and-bolts of providing informatics and computing support for clinical research. The subjects that the practitioner must be aware of are not only technological and scientific, but also organizational and managerial. Therefore, the author offers case studies based on real life experiences in order to prepare the readers for the challenges they may face during their experiences either supporting clinical research or supporting electronic record systems. Clinical research computing is the application of computational methods to the broad field of clinical research. With the advent of modern digital computing, and the powerful data collection, storage, and analysis that is possible with it, it becomes more relevant to understand the technical details in order to fully seize its opportunities. - Offers case studies, based on real-life examples where possible, to engage the readers with more complex examples - Provides studies backed by technical details, e.g., schema diagrams, code snippets or algorithms illustrating particular techniques, to give the readers confidence to employ the techniques described in their own settings - Offers didactic content organization and an increasing complexity through the chapters |
| data science in clinical research: The Prevention and Treatment of Missing Data in Clinical Trials National Research Council, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Panel on Handling Missing Data in Clinical Trials, 2010-12-21 Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data. |
| data science in clinical research: Leveraging Data Science for Global Health Leo Anthony Celi, Maimuna S. Majumder, Patricia Ordóñez, Juan Sebastian Osorio, Kenneth E. Paik, Melek Somai, 2020-07-31 This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient. |
| data science in clinical research: Practical Guide to Clinical Data Management Susanne Prokscha, 2011-10-26 The management of clinical data, from its collection during a trial to its extraction for analysis, has become a critical element in the steps to prepare a regulatory submission and to obtain approval to market a treatment. Groundbreaking on its initial publication nearly fourteen years ago, and evolving with the field in each iteration since then, |
| data science in clinical research: Sharing Clinical Research Data Institute of Medicine, Board on Health Care Services, Board on Health Sciences Policy, Roundtable on Translating Genomic-Based Research for Health, National Cancer Policy Forum, Forum on Neuroscience and Nervous System Disorders, Forum on Drug Discovery, Development, and Translation, 2013-06-07 Pharmaceutical companies, academic researchers, and government agencies such as the Food and Drug Administration and the National Institutes of Health all possess large quantities of clinical research data. If these data were shared more widely within and across sectors, the resulting research advances derived from data pooling and analysis could improve public health, enhance patient safety, and spur drug development. Data sharing can also increase public trust in clinical trials and conclusions derived from them by lending transparency to the clinical research process. Much of this information, however, is never shared. Retention of clinical research data by investigators and within organizations may represent lost opportunities in biomedical research. Despite the potential benefits that could be accrued from pooling and analysis of shared data, barriers to data sharing faced by researchers in industry include concerns about data mining, erroneous secondary analyses of data, and unwarranted litigation, as well as a desire to protect confidential commercial information. Academic partners face significant cultural barriers to sharing data and participating in longer term collaborative efforts that stem from a desire to protect intellectual autonomy and a career advancement system built on priority of publication and citation requirements. Some barriers, like the need to protect patient privacy, pre- sent challenges for both sectors. Looking ahead, there are also a number of technical challenges to be faced in analyzing potentially large and heterogeneous datasets. This public workshop focused on strategies to facilitate sharing of clinical research data in order to advance scientific knowledge and public health. While the workshop focused on sharing of data from preplanned interventional studies of human subjects, models and projects involving sharing of other clinical data types were considered to the extent that they provided lessons learned and best practices. The workshop objectives were to examine the benefits of sharing of clinical research data from all sectors and among these sectors, including, for example: benefits to the research and development enterprise and benefits to the analysis of safety and efficacy. Sharing Clinical Research Data: Workshop Summary identifies barriers and challenges to sharing clinical research data, explores strategies to address these barriers and challenges, including identifying priority actions and low-hanging fruit opportunities, and discusses strategies for using these potentially large datasets to facilitate scientific and public health advances. |
| data science in clinical research: Data Monitoring in Clinical Trials David L. DeMets, Curt D. Furberg, Lawrence M. Friedman, 2006-06-22 From the authors of Fundamentals of Clinical Trials which has sold over 15,000 copies world wide since its publication in 1998. No competition yet as the text does not focus on how to do clinical trials but on very specific situations that can be encountered during the process. |
| data science in clinical research: Clinical Trial Data Analysis Using R and SAS Ding-Geng (Din) Chen, Karl E. Peace, Pinggao Zhang, 2017-06-01 Review of the First Edition The goal of this book, as stated by the authors, is to fill the knowledge gap that exists between developed statistical methods and the applications of these methods. Overall, this book achieves the goal successfully and does a nice job. I would highly recommend it ...The example-based approach is easy to follow and makes the book a very helpful desktop reference for many biostatistics methods.—Journal of Statistical Software Clinical Trial Data Analysis Using R and SAS, Second Edition provides a thorough presentation of biostatistical analyses of clinical trial data with step-by-step implementations using R and SAS. The book’s practical, detailed approach draws on the authors’ 30 years’ experience in biostatistical research and clinical development. The authors develop step-by-step analysis code using appropriate R packages and functions and SAS PROCS, which enables readers to gain an understanding of the analysis methods and R and SAS implementation so that they can use these two popular software packages to analyze their own clinical trial data. What’s New in the Second Edition Adds SAS programs along with the R programs for clinical trial data analysis. Updates all the statistical analysis with updated R packages. Includes correlated data analysis with multivariate analysis of variance. Applies R and SAS to clinical trial data from hypertension, duodenal ulcer, beta blockers, familial andenomatous polyposis, and breast cancer trials. Covers the biostatistical aspects of various clinical trials, including treatment comparisons, time-to-event endpoints, longitudinal clinical trials, and bioequivalence trials. |
| data science in clinical research: Intelligence-Based Medicine Anthony C. Chang, 2020-06-27 Intelligence-Based Medicine: Data Science, Artificial Intelligence, and Human Cognition in Clinical Medicine and Healthcare provides a multidisciplinary and comprehensive survey of artificial intelligence concepts and methodologies with real life applications in healthcare and medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and the data science domains that is symmetric and balanced. The content consists of basic concepts of artificial intelligence and its real-life applications in a myriad of medical areas as well as medical and surgical subspecialties. It brings section summaries to emphasize key concepts delineated in each section; mini-topics authored by world-renowned experts in the respective key areas for their personal perspective; and a compendium of practical resources, such as glossary, references, best articles, and top companies. The goal of the book is to inspire clinicians to embrace the artificial intelligence methodologies as well as to educate data scientists about the medical ecosystem, in order to create a transformational paradigm for healthcare and medicine by using this emerging new technology. - Covers a wide range of relevant topics from cloud computing, intelligent agents, to deep reinforcement learning and internet of everything - Presents the concepts of artificial intelligence and its applications in an easy-to-understand format accessible to clinicians and data scientists - Discusses how artificial intelligence can be utilized in a myriad of subspecialties and imagined of the future - Delineates the necessary elements for successful implementation of artificial intelligence in medicine and healthcare |
| data science in clinical research: Data Science for Healthcare Sergio Consoli, Diego Reforgiato Recupero, Milan Petković, 2019-02-23 This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book. |
| data science in clinical research: Clinical Data-Mining Irwin Epstein, 2010 Clinical Data-Mining (CDM) involves the conceptualization, extraction, analysis, and interpretation of available clinical data for practice knowledge-building, clinical decision-making and practitioner reflection. Depending upon the type of data mined, CDM can be qualitative or quantitative; it is generally retrospective, but may be meaningfully combined with original data collection.Any research method that relies on the contents of case records or information systems data inevitably has limitations, but with proper safeguards these can be minimized. Among CDM's strengths however, are that it is unobtrusive, inexpensive, presents little risk to research subjects, and is ethically compatible with practitioner value commitments. When conducted by practitioners, CDM yields conceptual as well as data-driven insight into their own practice- and program-generated questions.This pocket guide, from a seasoned practice-based researcher, covers all the basics of conducting practitioner-initiated CDM studies or CDM doctoral dissertations, drawing extensively on published CDM studies and completed CDM dissertations from multiple social work settings in the United States, Australia, Israel, Hong Kong and the United Kingdom. In addition, it describes consulting principles for researchers interested in forging collaborative university-agency CDM partnerships, making it a practical tool for novice practitioner-researchers and veteran academic-researchers alike.As such, this book is an exceptional guide both for professionals conducting practice-based research as well as for social work faculty seeking an evidence-informed approach to practice-research integration. |
| data science in clinical research: Principles and Practice of Clinical Research John I. Gallin, Frederick P Ognibene, 2011-04-28 The second edition of this innovative work again provides a unique perspective on the clinical discovery process by providing input from experts within the NIH on the principles and practice of clinical research. Molecular medicine, genomics, and proteomics have opened vast opportunities for translation of basic science observations to the bedside through clinical research. As an introductory reference it gives clinical investigators in all fields an awareness of the tools required to ensure research protocols are well designed and comply with the rigorous regulatory requirements necessary to maximize the safety of research subjects. Complete with sections on the history of clinical research and ethics, copious figures and charts, and sample documents it serves as an excellent companion text for any course on clinical research and as a must-have reference for seasoned researchers.*Incorporates new chapters on Managing Conflicts of Interest in Human Subjects Research, Clinical Research from the Patient's Perspective, The Clinical Researcher and the Media, Data Management in Clinical Research, Evaluation of a Protocol Budget, Clinical Research from the Industry Perspective, and Genetics in Clinical Research *Addresses the vast opportunities for translation of basic science observations to the bedside through clinical research*Delves into data management and addresses how to collect data and use it for discovery*Contains valuable, up-to-date information on how to obtain funding from the federal government |
| data science in clinical research: Clinical Trial Data Analysis Using R Ding-Geng (Din) Chen, Karl E. Peace, 2010-12-14 Too often in biostatistical research and clinical trials, a knowledge gap exists between developed statistical methods and the applications of these methods. Filling this gap, Clinical Trial Data Analysis Using R provides a thorough presentation of biostatistical analyses of clinical trial data and shows step by step how to implement the statistical methods using R. The book’s practical, detailed approach draws on the authors’ 30 years of real-world experience in biostatistical research and clinical development. Each chapter presents examples of clinical trials based on the authors’ actual experiences in clinical drug development. Various biostatistical methods for analyzing the data are then identified. The authors develop analysis code step by step using appropriate R packages and functions. This approach enables readers to gain an understanding of the analysis methods and R implementation so that they can use R to analyze their own clinical trial data. With step-by-step illustrations of R implementations, this book shows how to easily use R to simulate and analyze data from a clinical trial. It describes numerous up-to-date statistical methods and offers sound guidance on the processes involved in clinical trials. |
| data science in clinical research: Clinical Data Management Richard K. Rondel, Sheila A. Varley, Colin F. Webb, 2000-02-03 Extensively revised and updated, with the addition of new chapters and authors, this long-awaited second edition covers all aspects of clinical data management. Giving details of the efficient clinical data management procedures required to satisfy both corporate objectives and quality audits by regulatory authorities, this text is timely and an important contribution to the literature. The volume: * is written by well-known and experienced authors in this area * provides new approaches to major topics in clinical data management * contains new chapters on systems software validation, database design and performance measures. It will be invaluable to anyone in the field within the pharmaceutical industry, and to all biomedical professionals working in clinical research. |
| data science in clinical research: The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry Stephanie K. Ashenden, 2021-04-23 The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows the journey that a drug company takes when producing a therapeutic, from the very beginning to ultimately benefitting a patient's life. This comprehensive resource will be useful to those working in the pharmaceutical industry, but will also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics. - Demonstrates how the prediction of toxic effects is performed, how to reduce costs in testing compounds, and its use in animal research - Written by the industrial teams who are conducting the work, showcasing how the technology has improved and where it should be further improved - Targets materials for a better understanding of techniques from different disciplines, thus creating a complete guide |
| data science in clinical research: Clinical Research Informatics Rachel Richesson, James Andrews, 2012-02-15 The purpose of the book is to provide an overview of clinical research (types), activities, and areas where informatics and IT could fit into various activities and business practices. This book will introduce and apply informatics concepts only as they have particular relevance to clinical research settings. |
| data science in clinical research: Statistics for Health Data Science Ruth Etzioni, Micha Mandel, Roman Gulati, 2021-01-04 Students and researchers in the health sciences are faced with greater opportunity and challenge than ever before. The opportunity stems from the explosion in publicly available data that simultaneously informs and inspires new avenues of investigation. The challenge is that the analytic tools required go far beyond the standard methods and models of basic statistics. This textbook aims to equip health care researchers with the most important elements of a modern health analytics toolkit, drawing from the fields of statistics, health econometrics, and data science. This textbook is designed to overcome students’ anxiety about data and statistics and to help them to become confident users of appropriate analytic methods for health care research studies. Methods are presented organically, with new material building naturally on what has come before. Each technique is motivated by a topical research question, explained in non-technical terms, and accompanied by engaging explanations and examples. In this way, the authors cultivate a deep (“organic”) understanding of a range of analytic techniques, their assumptions and data requirements, and their advantages and limitations. They illustrate all lessons via analyses of real data from a variety of publicly available databases, addressing relevant research questions and comparing findings to those of published studies. Ultimately, this textbook is designed to cultivate health services researchers that are thoughtful and well informed about health data science, rather than data analysts. This textbook differs from the competition in its unique blend of methods and its determination to ensure that readers gain an understanding of how, when, and why to apply them. It provides the public health researcher with a way to think analytically about scientific questions, and it offers well-founded guidance for pairing data with methods for valid analysis. Readers should feel emboldened to tackle analysis of real public datasets using traditional statistical models, health econometrics methods, and even predictive algorithms. Accompanying code and data sets are provided in an author site: https://roman-gulati.github.io/statistics-for-health-data-science/ |
| data science in clinical research: Designing Clinical Research Stephen B. Hulley, Steven R. Cummings, Warren S. Browner, Deborah G. Grady, Thomas B. Newman, 2011-11-30 Designing Clinical Research sets the standard for providing a practical guide to planning, tabulating, formulating, and implementing clinical research, with an easy-to-read, uncomplicated presentation. This edition incorporates current research methodology—including molecular and genetic clinical research—and offers an updated syllabus for conducting a clinical research workshop. Emphasis is on common sense as the main ingredient of good science. The book explains how to choose well-focused research questions and details the steps through all the elements of study design, data collection, quality assurance, and basic grant-writing. All chapters have been thoroughly revised, updated, and made more user-friendly. |
| data science in clinical research: Basic Science Methods for Clinical Researchers Morteza Jalali, Francesca Yvonne Louise Saldanha, Mehdi Jalali, 2017-03-31 Basic Science Methods for Clinical Researchers addresses the specific challenges faced by clinicians without a conventional science background. The aim of the book is to introduce the reader to core experimental methods commonly used to answer questions in basic science research and to outline their relative strengths and limitations in generating conclusive data. This book will be a vital companion for clinicians undertaking laboratory-based science. It will support clinicians in the pursuit of their academic interests and in making an original contribution to their chosen field. In doing so, it will facilitate the development of tomorrow's clinician scientists and future leaders in discovery science. - Serves as a helpful guide for clinical researchers who lack a conventional science background - Organized around research themes pertaining to key biological molecules, from genes, to proteins, cells, and model organisms - Features protocols, techniques for troubleshooting common problems, and an explanation of the advantages and limitations of a technique in generating conclusive data - Appendices provide resources for practical research methodology, including legal frameworks for using stem cells and animals in the laboratory, ethical considerations, and good laboratory practice (GLP) |
| data science in clinical research: Preventing and Treating Missing Data in Longitudinal Clinical Trials Craig H. Mallinckrodt, 2013-01-28 Focuses on the prevention and treatment of missing data in longitudinal clinical trials, looking at key principles and explaining analytic methods. |
| data science in clinical research: Data Analytics in Biomedical Engineering and Healthcare Kun Chang Lee, Sanjiban Sekhar Roy, Pijush Samui, Vijay Kumar, 2020-10-18 Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. - Examines the development and application of data analytics applications in biomedical data - Presents innovative classification and regression models for predicting various diseases - Discusses genome structure prediction using predictive modeling - Shows readers how to develop clinical decision support systems - Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks |
| data science in clinical research: Design of Experiments and Advanced Statistical Techniques in Clinical Research Basavarajaiah D. M., Bhamidipati Narasimha Murthy, 2020-11-05 Recent Statistical techniques are one of the basal evidence for clinical research, a pivotal in handling new clinical research and in evaluating and applying prior research. This book explores various choices of statistical tools and mechanisms, analyses of the associations among different clinical attributes. It uses advanced statistical methods to describe real clinical data sets, when the clinical processes being examined are still in the process. This book also discusses distinct methods for building predictive and probability distribution models in clinical situations and ways to assess the stability of these models and other quantitative conclusions drawn by realistic experimental data sets. Design of experiments and recent posthoc tests have been used in comparing treatment effects and precision of the experimentation. This book also facilitates clinicians towards understanding statistics and enabling them to follow and evaluate the real empirical studies (formulation of randomized control trial) that pledge insight evidence base for clinical practices. This book will be a useful resource for clinicians, postgraduates scholars in medicines, clinical research beginners and academicians to nurture high-level statistical tools with extensive scope. |
| data science in clinical research: Data Science and Medical Informatics in Healthcare Technologies Nguyen Thi Dieu Linh, Zhongyu (Joan) Lu, 2021-06-19 This book highlights a timely and accurate insight at the endeavour of the bioinformatics and genomics clinicians from industry and academia to address the societal needs. The contents of the book unearth the lacuna between the medication and treatment in the current preventive medicinal and pharmaceutical system. It contains chapters prepared by experts in life sciences along with data scientists for examining the circumstances of health care system for the next decade. It also highlights the automated processes for analyzing data in clinical trial research, specifically for drug development. Additionally, the data science solutions provided in this book help pharmaceutical companies to improve on what had historically been manual, costly and laborious process for cross-referencing research in clinical trials on drug development, while laying the groundwork for use with a full range of other drugs for the conditions ranging from tuberculosis, to diabetes, to heart attacks and many others. |
| data science in clinical research: Critical Thinking in Clinical Research Felipe Fregni, Ben M. W. Illigens, 2018 Critical Thinking in Clinical Research explains the fundamentals of clinical research in a case-based approach. The core concept is to combine a clear and concise transfer of information and knowledge with an engagement of the reader to develop a mastery of learning and critical thinking skills. The book addresses the main concepts of clinical research, basics of biostatistics, advanced topics in applied biostatistics, and practical aspects of clinical research, with emphasis on clinical relevance across all medical specialties. |
| data science in clinical research: Clinical Data as the Basic Staple of Health Learning Institute of Medicine, Roundtable on Value and Science-Driven Health Care, 2011-01-14 Successful development of clinical data as an engine for knowledge generation has the potential to transform health and health care in America. As part of its Learning Health System Series, the Roundtable on Value & Science-Driven Health Care hosted a workshop to discuss expanding the access to and use of clinical data as a foundation for care improvement. |
| data science in clinical research: Fundamentals of Clinical Trials Lawrence M. Friedman, Curt Furberg, David L. DeMets, 1998 This classic reference, now updated with the newest applications and results, addresses the fundamentals of such trials based on sound scientific methodology, statistical principles, and years of accumulated experience by the three authors. |
| data science in clinical research: Strategy and Statistics in Clinical Trials Joseph Tal, 2011-07-14 Delineates the statistical building blocks and concepts of clinical trials. |
| data science in clinical research: A Practical Guide to Managing Clinical Trials JoAnn Pfeiffer, Cris Wells, 2017-05-18 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. |
| data science in clinical research: Data Science in the Medical Field Seifedine Kadry, Shubham Mahajan, 2024-09-30 ata science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage. • Shows how improving automated analytical techniques can be used to generate new information from data for healthcare applications• Combines a number of related fields, with a particular emphasis on machine learning, big data analytics, statistics, pattern recognition, computer vision, and semantic web technologies• Provides information on the cutting-edge data science tools required to accelerate innovation for healthcare organizations and patients by reading this book |
| data science in clinical research: Advancing the Nation's Health Needs National Research Council, Policy and Global Affairs, Board on Higher Education and Workforce, Committee for Monitoring the Nation's Changing Needs for Biomedical, Behavioral, and Clinical Personnel, 2005-08-13 This report is the twelfth assessment of the National Institutes of Health National Research Service Awards program. The research training needs of the country in basic biomedical, clinical, and behavioral and social sciences are considered. Also included are the training needs of oral health, nursing, and health services research. The report has been broadly constructed to take into account the rapidly evolving national and international health care needs. The past and present are analyzed, and predictions with regard to future needs are presented. |
| data science in clinical research: The Comprehensive Guide To Clinical Research Chris Sauber, Dan Sfera, 2019-04-21 Condensing the most important topics in all of clinical research in an easy to understand presentation. The 20 percent of what you need to know in order to be 80 percent proficient!The authors who have operated various levels of businesses in the clinical research industry since 2005 believe that more practical information pertaining to clinical research needs to be accessible to individuals who are new to the industry or are curious about entering the rewarding world of clinical trials.This book reads in an easy to understand style and is based on proven methods the authors have developed to train their own employees and students of their various clinical research academies throughout the years. Picking this up and absorbing the information will allow anyone to gain much better insight into the complicated dynamics of clinical research. This practical roadmap is all you will need to get started on your clinical trial journey!In this book you will learn about:Regulations and the history as well as evolution of GCP.Clinical Research Site OperationsMonitoring Dynamics and Typical Monitoring VistsCRO ActivitiesSponsor Level DynamicsIndustry VendorsCommon Career Opportunities and Employment Roadmaps |
| data science in clinical research: Common Statistical Methods for Clinical Research with SAS Examples, Third Edition Glenn Walker, Jack Shostak, 2010-02-15 Glenn Walker and Jack Shostak's Common Statistical Methods for Clinical Research with SAS Examples, Third Edition, is a thoroughly updated edition of the popular introductory statistics book for clinical researchers. This new edition has been extensively updated to include the use of ODS graphics in numerous examples as well as a new emphasis on PROC MIXED. Straightforward and easy to use as either a text or a reference, the book is full of practical examples from clinical research to illustrate both statistical and SAS methodology. Each example is worked out completely, step by step, from the raw data. Common Statistical Methods for Clinical Research with SAS Examples, Third Edition, is an applications book with minimal theory. Each section begins with an overview helpful to nonstatisticians and then drills down into details that will be valuable to statistical analysts and programmers. Further details, as well as bonus information and a guide to further reading, are presented in the extensive appendices. This text is a one-source guide for statisticians that documents the use of the tests used most often in clinical research, with assumptions, details, and some tricks--all in one place. This book is part of the SAS Press program. |
| data science in clinical research: Confidence Intervals for Discrete Data in Clinical Research Vivek Pradhan, Ashis Gangopadhyay, Sandeep M. Menon, Cynthia Basu, Tathagata Banerjee, 2021-11-15 Confidence Intervals for Discrete Data in Clinical Research is designed as a toolbox for biomedical researchers. Analysis of discrete data is one of the most used yet vexing areas in clinical research. The array of methodologies available in the literature to address the inferential questions for binomial and multinomial data can be a double-edged sword. On the one hand, these methods open a rich avenue of exploration of data; on the other, the wide-ranging and competing methodologies potentially lead to conflicting inferences, adding to researchers' confusion and frustration and also leading to reporting bias. This book addresses the problems that many practitioners experience in choosing and implementing fit for purpose data analysis methods to answer critical inferential questions for binomial and count data. The book is an outgrowth of the authors' collective experience in biomedical research and provides an excellent overview of inferential questions of interest for binomial proportions and rates based on count data, and reviews various solutions to these problems available in the literature. Each chapter discusses the strengths and weaknesses of the methods and suggests practical recommendations. The book's primary focus is on applications in clinical research, and the goal is to provide direct benefit to the users involved in the biomedical field. |
| data science in clinical research: Big Data-Enabled Nursing Connie W. Delaney, Charlotte A. Weaver, Judith J. Warren, Thomas R. Clancy, Roy L. Simpson, 2017-11-02 Historically, nursing, in all of its missions of research/scholarship, education and practice, has not had access to large patient databases. Nursing consequently adopted qualitative methodologies with small sample sizes, clinical trials and lab research. Historically, large data methods were limited to traditional biostatical analyses. In the United States, large payer data has been amassed and structures/organizations have been created to welcome scientists to explore these large data to advance knowledge discovery. Health systems electronic health records (EHRs) have now matured to generate massive databases with longitudinal trending. This text reflects how the learning health system infrastructure is maturing, and being advanced by health information exchanges (HIEs) with multiple organizations blending their data, or enabling distributed computing. It educates the readers on the evolution of knowledge discovery methods that span qualitative as well as quantitative data mining, including the expanse of data visualization capacities, are enabling sophisticated discovery. New opportunities for nursing and call for new skills in research methodologies are being further enabled by new partnerships spanning all sectors. |
| data science in clinical research: Demystifying Big Data and Machine Learning for Healthcare Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz, 2017-02-15 Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them. |
| data science in clinical research: Understanding Clinical Research Renato D. Lopes, Robert A. Harrington, 2013-05-22 A complete guide to understanding and applying clinical research results Ideal for both researchers and healthcare providers Understanding Clinical Research addresses both the operational challenges of clinical trials and the needs of clinicians to comprehend the nuances of research methods to accurately analyze study results. This timely resource covers all aspects of clinical trials--from study design and statistics to regulatory oversight--and it delivers a detailed yet streamlined overview of must-know research topics. The text features an accessible three-part organization that traces the evolution of clinical research and explains the bedrock principles and unique challenges of clinical experimentation and observational research. Reinforcing this content are real-life case examples--drawn from the authors' broad experience--that put chapter concepts into action and contribute to a working knowledge of integral research techniques. FEATURES: The most definitive guide to promoting excellence in clinical research, designed to empower healthcare providers to assess a study's strengths and weaknesses with confidence and apply this knowledge to optimize patient outcomes In-depth coverage of fundamental research methods and protocols from preeminent authorities provides readers with an instructive primer and a springboard for ongoing clinical research education Clear, comprehensive three-part organization: Section One: Evolution of Clinical Research offers a succinct history of clinical trials, drug regulations, and the role of the FDA while covering the impact of information technology and academic research organizations Section Two: Principles of Clinical Experimentation takes you through the typical phases of clinical trials in the development of medical products, from initial human subject research to postapproval surveillance studies Section Three: Observational Research highlights the underlying principles, pitfalls, and methods for case-control studies, cohort studies, registries, and subgroup analyses within randomized trials |
| data science in clinical research: Translational Biomedical Informatics Bairong Shen, Haixu Tang, Xiaoqian Jiang, 2016-10-31 This book introduces readers to essential methods and applications in translational biomedical informatics, which include biomedical big data, cloud computing and algorithms for understanding omics data, imaging data, electronic health records and public health data. The storage, retrieval, mining and knowledge discovery of biomedical big data will be among the key challenges for future translational research. The paradigm for precision medicine and healthcare needs to integratively analyze not only the data at the same level – e.g. different omics data at the molecular level – but also data from different levels – the molecular, cellular, tissue, clinical and public health level. This book discusses the following major aspects: the structure of cross-level data; clinical patient information and its shareability; and standardization and privacy. It offers a valuable guide for all biologists, biomedical informaticians and clinicians with an interest in Precision Medicine Informatics. |
| data science in clinical research: Oxford Handbook of Medical Statistics Janet Peacock, Philip Peacock, 2011 The majority of medical research involves quantitative methods and so it is essential to be able to understand and interpret statistics. This book shows readers how to develop the skills required to critically appraise research evidence effectively, and how to conduct research and communicate their findings. |
| data science in clinical research: Foundations of Clinical Research Leslie Gross Portney, Mary P. Watkins, 2015 Draw upon the foundations necessary for finding and interpreting research evidence across all healthcare professions. Revised to reflect the most current changes in the field of clinical research in rehabilitation and medicine, you'll find a growing emphasis on evidence-based practice (EBP) as well as new vocabulary that is being integrated into research and practice across disciplines. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
A phase II, multicenter, nonblinded, randomized controlled …
Jul 9, 2024 · 5 Department of Data Science, Clinical Research Center, National Center for Child Health and Development, Setagaya-ku, Tokyo, Japan, 6 Department of Developmental …
Management of Data and Information in Research
projects completed by students, retaining research data for 12 months after the completion of the project may be sufficient • for most clinical trials, retaining research data for 15 years or more …
A primer on synthetic health data - arXiv.org
1Center for Health Data Science, University of Copenhagen, Copenhagen, Denmark 2Department of Clinical Medicine, Aalborg University, Aalborg, ... In the health sector, pseudonymized health …
Source Data Verification (SDV) quality in clinical research: A …
Clinical research; clinical trial monitoring; quality; Source Data Verification Corresponding author: M. Hamidi; Email: hamidim@uthscsa.edu ©TheAuthor(s),2024.PublishedbyCambridge …
Clinical Research Coordinator Training - blog.amf
The Clinical Researcher and the Media, Data Management in Clinical Research, Evaluation of a Protocol Budget, Clinical Research from the Industry Perspective, and Genetics in Clinical …
Annals of Oncology abstracts
4Department of Clinical Data Science, Clinical Research & Education Promotion Divi-sion, National Center of Neurology and Psychiatry, Kodaira, Japan; 5Department of Breast Medical …
GRADUATE STUDENT Contents` HANDBOOK - University of …
of the medical center: education, research, clinical care and community. A respectful and professional learning environment is an important reflection of our ICARE values.
Research Assistant in Neuromodulation and Neuroimaging
Feb 5, 2024 · including data analysis or computer science. The individual will assist with a clinical trial using both ... postdoctoral fellow, the post-bac clinical research assistant will carry out a …
GRADUATE STUDENT Contents` HANDBOOK - University of …
3 DEPARTMENT EDUCATION POLICIES LEAVE OF ABSENCE Upon the recommendation of the Program Director, the Dean may grant a leave of absence to a matriculated graduate …
WAE FOREST UNIVERSIT MASTER’S IN CLINICAL RESEARCH …
to tackle complex clinical research challenges within the context of the larger health care system. Ralph D’Agostino, Jr., PhD, FASA Program Director of Master’s in Clinical Research …
Advancing Translation of Clinical Research Into Practice and …
science may be incorporated into clinical trials research, particularly pragmatic trials, to improve the relevance and impact of scientific innovation. This thematic review intends to raise …
The Ohio State University
principles to facilitate the analysis of multi-dimensional data; the generation of information and knowledge from component sources using the principles of . data science and data analytics; …
GRADUATE STUDENT Contents` HANDBOOK - University of …
Sep 22, 2020 · 1 University of Rochester School of Medicine and Dentistry Department of Public Health Sciences Contents` • Section 1 Message from the Director of Education GRADUATE …
Organizational Restructuring - 田辺三菱製薬株式会社
Laboratories, DMPK Research Laboratories and Safety Research Laboratories will be restructured to come under the direct supervision of Sohyaku. Innovative Research Di vision. …
Clinical Research Methodology for Data Science
CLINICAL RESEARCH •Research that is done in “clinical” setting •Related to patients or people with or without medical conditions •Contrasts to “preclinical research” •Research in parts of …
SAS® Academy for Clinical Programming˜
bination of coursework and hands-on learning helps you build a foundation of clinical research theory and data analysis skills – which can open doors to new opportunities.˚ ... Modern Data …
FREE ONLINE RESEARCH METHODS COURSES - University of …
FREE ONLINE RESEARCH METHODS COURSES . Topic Course topic Link to the course General
Conduct of Research - National Institutes of Health
The science, clinical care, training, and ... record-keeping, the use of electronic research records, data sharing and use policies, mentoring, dual use research of concern, and authorship. We …
Clinical Research and Clinical Trials: An In-Depth Exploration
Clinical Research and Clinical Trials: An In-Depth Eploration. Clin Res. ():. Clinical Research and Clinical Trials: An In-Depth Exploration Ashish Pandey* Daswani Dental College, Rajasthan …
Recommendations for Data Standards in Health Data …
in Health Data Research ... HDR UK is the UK’s national institute for health data science with a mission to unite the UK’s health data to enable discoveries that improve people’s lives, so that …
Adjustment of Conditional Bias in Hazard Ratios for Group …
RESEARCH ARTICLE OPEN ACCESS Adjustment of Conditional Bias in Hazard Ratios for Group Sequential Testing of Progression-Free Survival and ... Biostatistics, Department of …
Healthcare Data Analytics - OHSU
3 Healthcare Data Analytics WILLIAM R. HERSH Learning Objectives After&reading&this&chapter&the&reader&should&be&able&to:& • Discuss the difference …
A phase I/IIa trial of atorvastatin in Japanese patients with …
Department of Data Science, Clinical Research Center, National Center for Child Health and Development, 2-10-1, Okura, Setagaya-ku, Tokyo, 157-8535, Japan.
2023 Salary Report Clinical Research - CK Group
Clinical Research and Medical Affairs ... Medical Science Liaison 46 Medical Compliance and Governance 48 ... Clinical Data Managers 86 SAS Programmers 87 Conclusion 88 Limitations …
NISOplus2022MilesConradlecture:The …
P.F. Brennan / NISO plus 2022 Miles Conrad lecture: The role of a library in a world of unstructured data 411 3. The changing nature of research across NIH
Bierman Autism Centers Unveils Clinical Research
Jun 2, 2025 · Every data point our clinicians record can power a A Bierman clinician leads an internal training session on upcoming projects. A Bierman Autism Centers clinician presents …
Clinical Technologies Concepts Methodologies Tools And …
Tools And Applications Contemporary Research In Information Science And Technology Revolutionizing Healthcare: A Deep Dive into Clinical Technologies ... from unstructured …
Data Quality Assurance in Clinical Trials: Best Practices and …
and complete.Data quality assurance in clinical trials is a multifaceted endeavor that is crucial for the reliability and validity of research outcomes. Implementing best practices and addressing …
Characterizing Pain Points in Clinical Data Management and …
A recent white paper by the Society for Clinical Data Management (SCDM) challenged the drug development enterprise to nurture “true technology innovation” [2 ]. Rec-ommendations …
DEVELOPING A PERSONALIZED MEDICINE APPROACH FOR …
Proposed work for personalized medicine approach for clinical trial design and patient selection using machine learning Data Collection and Preprocessing: Source data from various …
DATA SHARING AGREEMENT - ClinicalStudyDataRequest.com
1.8 “Research Proposal” means the research proposal as submitted by the Lead Researcher approved by the Independent Review Panel, including the plan for analyses of the …
Clinical research study protocols: an overview of the different …
“Clinical trial means a research project in which persons are prospectively assigned to a health- related intervention in order to investigate its effects on health or on the structure and function …
Basic Principles of Clinical Research and Methodology
Opp. Mohota Science College, Umred Road, Nagpur 440 009 (MS) Phones: Rel: 3245220, Fax: 0712-2704275 ... Clinical Data Management CK Katiyar Ph.D Director - Herbal Drug Research …
Generation of PLASE score for patent ductus arteriosus using …
3Department of Data Science, Clinical Research Center, National Center for Child Health and Development, Setagaya, Japan. 4Department of Neonatology, Graduate School of
HANDBOOK FOR GOOD CLINICAL RESEARCH PRACTICE …
Good Clinical Research Practice (GCP) is a process that incorporates established ethical and scientifi c quality standards for the design, conduct, recording and reporting of clinical …
Data Collection Methods and Tools for Research; - hal.science
Data Collection, Research Methodology, Data Collection Methods, Academic Research Paper, Data Collection Techniques. I. INTRODUCTION Different methods for gathering information …
The association between emotion malleability beliefs and …
Japan, 3Department of Clinical Data Science, Clinical Research and Education Promotion Division, National Center of Neurology and Psychiatry, Tokyo, Japan, 4Graduate School of …
Interventions for Shiga toxin-producing Escherichia coli
Feb 4, 2022 · Data Science, Clinical Research Center, National Center for Child Health and Development, Tokyo, Japan, 6 Infectious Disease Surveillance Center, National Institute of …
Clinical Utility of Circulating Tumor DNA in Advanced Rare …
Clinical Utility of Circulating Tumor DNA in Advanced Rare Cancers Hitomi Sumiyoshi Okuma1,2*, Kan Yonemori1, Yuki Kojima1, Maki Tanioka1, Kazuki Sudo1, Emi Noguchi1, Susumu Hijioka3, …
Machine Learning and Artificial Intelligence in Pharmaceutical …
the research, science, and processes associated with drug devel-opment, starting with drug discovery to clinical development and conduct, and nally the life-cycle management stage. ...
HANDBOOK FOR GOOD CLINICAL RESEARCH PRACTICE (GCP)
Good Clinical Research Practice (GCP) is a process that incorporates established ethical and scientifi c quality standards for the design, conduct, recording and reporting of clinical …
Open science: The open clinical trials data journey
Open science, data sharing, individual participant data, transparency History of openness and transparency Data sharing has a long history of clinical investigators realizing the importance …
One-Year Incidences of Venous Thromboembolism, Bleeding, …
Aug 8, 2022 · This was a nationwide, multicenter clinical registry conducted in accordance with the Declaration of Helsinki and the Ethical Guidelines for Medical Science Studies on Human …
Transparency in Clinical Trials - Pfizer
protect participant privacy, preserve regulatory authority, and maintain incentives for those who generate research data. Pfizer offers access to the clinical data gathered in company …
Building Quality into the Design and Conduct of Clinical …
Jan 31, 2024 · Mr. Nadolny is the Global head of Clinical Data Management at Sanofi which include Centralized Monitoring.In addition to being the Chair of the SCDM board, he led the …
Annals of Oncology abstracts
4Department of Clinical Data Science, Clinical Research & Education Promotion Divi-sion, National Center of Neurology and Psychiatry, Kodaira, Japan; 5Department of Breast Medical …
Clinical Quality Management System (CQMS): A Framework …
is defined yet, as there is one final product for clinical research, results data, shortcomings in the quality constituent data could have an impact on the TCQ of the final results. ... based on …
VHA Handbook 1200.12, Use of Data and Data Repositories …
and the development of VHA research data repositories. 2. SUMMARY OF MAJOR CHANGES. This new VHA Handbook addresses both the use of data for research and clinical and …
Early Aggressive Intervention for Infantile Atopic Dermatitis to ...
Clinical Trials Registry: UMIN-CTR Registry (registration number: UMIN000028043) Corresponding Study Protocol version number: Ver. 1.5 ... Department of Data Science, …
A Preliminary Opinion on data protection and scientific …
research. Data protection is intended to serve as a safety net for individuals whose data are needed to support science: personal data enable better understanding of diseases, the …
