| biomarker validation study design: How We Do Harm Otis Webb Brawley, MD, Paul Goldberg, 2012-01-31 A startling and important exposé on the state of medicine, research, and healthcare today by the Chief Medical and Scientific Officer of the American Cancer Society How We Do Harm exposes the underbelly of healthcare today—the overtreatment of the rich, the under treatment of the poor, the financial conflicts of interest that determine the care that physicians' provide, insurance companies that don't demand the best (or even the least expensive) care, and pharmaceutical companies concerned with selling drugs, regardless of whether they improve health or do harm. Dr. Otis Brawley is the chief medical and scientific officer of The American Cancer Society, an oncologist with a dazzling clinical, research, and policy career. How We Do Harm pulls back the curtain on how medicine is really practiced in America. Brawley tells of doctors who select treatment based on payment they will receive, rather than on demonstrated scientific results; hospitals and pharmaceutical companies that seek out patients to treat even if they are not actually ill (but as long as their insurance will pay); a public primed to swallow the latest pill, no matter the cost; and rising healthcare costs for unnecessary—and often unproven—treatments that we all pay for. Brawley calls for rational healthcare, healthcare drawn from results-based, scientifically justifiable treatments, and not just the peddling of hot new drugs. Brawley's personal history – from a childhood in the gang-ridden streets of black Detroit, to the green hallways of Grady Memorial Hospital, the largest public hospital in the U.S., to the boardrooms of The American Cancer Society—results in a passionate view of medicine and the politics of illness in America - and a deep understanding of healthcare today. How We Do Harm is his well-reasoned manifesto for change. |
| biomarker validation study design: Proteomic and Metabolomic Approaches to Biomarker Discovery Haleem J. Issaq, 2013-05-20 Proteomic and Metabolomic Approaches to Biomarker Discovery demonstrates how to leverage biomarkers to improve accuracy and reduce errors in research. Disease biomarker discovery is one of the most vibrant and important areas of research today, as the identification of reliable biomarkers has an enormous impact on disease diagnosis, selection of treatment regimens, and therapeutic monitoring. Various techniques are used in the biomarker discovery process, including techniques used in proteomics, the study of the proteins that make up an organism, and metabolomics, the study of chemical fingerprints created from cellular processes. Proteomic and Metabolomic Approaches to Biomarker Discovery is the only publication that covers techniques from both proteomics and metabolomics and includes all steps involved in biomarker discovery, from study design to study execution. The book describes methods, and presents a standard operating procedure for sample selection, preparation, and storage, as well as data analysis and modeling. This new standard effectively eliminates the differing methodologies used in studies and creates a unified approach. Readers will learn the advantages and disadvantages of the various techniques discussed, as well as potential difficulties inherent to all steps in the biomarker discovery process. A vital resource for biochemists, biologists, analytical chemists, bioanalytical chemists, clinical and medical technicians, researchers in pharmaceuticals, and graduate students, Proteomic and Metabolomic Approaches to Biomarker Discovery provides the information needed to reduce clinical error in the execution of research. - Describes the use of biomarkers to reduce clinical errors in research - Includes techniques from a range of biomarker discoveries - Covers all steps involved in biomarker discovery, from study design to study execution |
| biomarker validation study design: Evolution of Translational Omics Institute of Medicine, Board on Health Sciences Policy, Board on Health Care Services, Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials, 2012-09-13 Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials. |
| biomarker validation study design: Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease Institute of Medicine, Food and Nutrition Board, Board on Health Sciences Policy, Board on Health Care Services, Committee on Qualification of Biomarkers and Surrogate Endpoints in Chronic Disease, 2010-06-25 Many people naturally assume that the claims made for foods and nutritional supplements have the same degree of scientific grounding as those for medication, but that is not always the case. The IOM recommends that the FDA adopt a consistent scientific framework for biomarker evaluation in order to achieve a rigorous and transparent process. |
| biomarker validation study design: Biomarkers in Drug Development Michael R. Bleavins, Claudio Carini, Mallé Jurima-Romet, Ramin Rahbari, 2011-09-20 Discover how biomarkers can boost the success rate of drug development efforts As pharmaceutical companies struggle to improve the success rate and cost-effectiveness of the drug development process, biomarkers have emerged as a valuable tool. This book synthesizes and reviews the latest efforts to identify, develop, and integrate biomarkers as a key strategy in translational medicine and the drug development process. Filled with case studies, the book demonstrates how biomarkers can improve drug development timelines, lower costs, facilitate better compound selection, reduce late-stage attrition, and open the door to personalized medicine. Biomarkers in Drug Development is divided into eight parts: Part One offers an overview of biomarkers and their role in drug development. Part Two highlights important technologies to help researchers identify new biomarkers. Part Three examines the characterization and validation process for both drugs and diagnostics, and provides practical advice on appropriate statistical methods to ensure that biomarkers fulfill their intended purpose. Parts Four through Six examine the application of biomarkers in discovery, preclinical safety assessment, clinical trials, and translational medicine. Part Seven focuses on lessons learned and the practical aspects of implementing biomarkers in drug development programs. Part Eight explores future trends and issues, including data integration, personalized medicine, and ethical concerns. Each of the thirty-eight chapters was contributed by one or more leading experts, including scientists from biotechnology and pharmaceutical firms, academia, and the U.S. Food and Drug Administration. Their contributions offer pharmaceutical and clinical researchers the most up-to-date understanding of the strategies used for and applications of biomarkers in drug development. |
| biomarker validation study design: Bayesian Adaptive Methods for Clinical Trials Scott M. Berry, Bradley P. Carlin, J. Jack Lee, Peter Muller, 2010-07-19 Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer's disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adapti |
| biomarker validation study design: The Detection of Biomarkers Sibel A. Ozkan, Nurgul K. Bakirhan, Fariba Mollarasouli, 2021-12-05 Reliable, precise and accurate detection and analysis of biomarkers remains a significant challenge for clinical researchers. Methods for the detection of biomarkers are rather complex, requiring pre-treatment steps before analysis can take place. Moreover, comparing various biomarker assays and tracing research progress in this area systematically is a challenge for researchers. The Detection of Biomarkers presents developments in biomarker detection, including methods tools and strategies, biosensor design, materials, and applications. The book presents methods, materials and procedures that are simple, precise, sensitive, selective, fast and economical, and therefore highly practical for use in clinical research scenarios. This volume situates biomarker detection in its research context and sets out future prospects for the area. Its 20 chapters offer a comprehensive coverage of biomarkers, including progress on nanotechnology, biosensor types, synthesis, immobilization, and applications in various fields. The book also demonstrates, for students, how to synthesize and immobilize biosensors for biomarker assay. It offers researchers real alternative and innovative ways to think about the field of biomarker detection, increasing the reliability, precision and accuracy of biomarker detection. - Locates biomarker detection in its research context, setting out present and future prospects - Allows clinical researchers to compare various biomarker assays systematically - Presents new methods, materials and procedures that are simple, precise, sensitive, selective, fast and economical - Gives innovative biomarker assays that are viable alternatives to current complex methods - Helps clinical researchers who need reliable, precise and accurate biomarker detection methods |
| biomarker validation study design: Group Sequential Methods with Applications to Clinical Trials Christopher Jennison, Bruce W. Turnbull, 1999-09-15 Group sequential methods answer the needs of clinical trial monitoring committees who must assess the data available at an interim analysis. These interim results may provide grounds for terminating the study-effectively reducing costs-or may benefit the general patient population by allowing early dissemination of its findings. Group sequential methods provide a means to balance the ethical and financial advantages of stopping a study early against the risk of an incorrect conclusion. Group Sequential Methods with Applications to Clinical Trials describes group sequential stopping rules designed to reduce average study length and control Type I and II error probabilities. The authors present one-sided and two-sided tests, introduce several families of group sequential tests, and explain how to choose the most appropriate test and interim analysis schedule. Their topics include placebo-controlled randomized trials, bio-equivalence testing, crossover and longitudinal studies, and linear and generalized linear models. Research in group sequential analysis has progressed rapidly over the past 20 years. Group Sequential Methods with Applications to Clinical Trials surveys and extends current methods for planning and conducting interim analyses. It provides straightforward descriptions of group sequential hypothesis tests in a form suited for direct application to a wide variety of clinical trials. Medical statisticians engaged in any investigations planned with interim analyses will find this book a useful and important tool. |
| biomarker validation study design: Drug Discovery and Evaluation: Methods in Clinical Pharmacology H.Gerhard Vogel, Jochen Maas, Alexander Gebauer, 2010-12-15 Drug Discovery and Evaluation has become a more and more difficult, expensive and time-consuming process. The effect of a new compound has to be detected by in vitro and in vivo methods of pharmacology. The activity spectrum and the potency compared to existing drugs have to be determined. As these processes can be divided up stepwise we have designed a book series Drug Discovery and Evaluation in the form of a recommendation document. The methods to detect drug targets are described in the first volume of this series Pharmacological Assays comprising classical methods as well as new technologies. Before going to man, the most suitable compound has to be selected by pharmacokinetic studies and experiments in toxicology. These preclinical methods are described in the second volume „Safety and Pharmacokinetic Assays. Only then are first studies in human beings allowed. Special rules are established for Phase I studies. Clinical pharmacokinetics are performed in parallel with human studies on tolerability and therapeutic effects. Special studies according to various populations and different therapeutic indications are necessary. These items are covered in the third volume: „Methods in Clinical Pharmacology. |
| biomarker validation study design: Analysis of Biomarker Data Stephen W. Looney, Joseph L. Hagan, 2015-03-16 A “how to” guide for applying statistical methods to biomarker data analysis Presenting a solid foundation for the statistical methods that are used to analyze biomarker data, Analysis of Biomarker Data: A Practical Guide features preferred techniques for biomarker validation. The authors provide descriptions of select elementary statistical methods that are traditionally used to analyze biomarker data with a focus on the proper application of each method, including necessary assumptions, software recommendations, and proper interpretation of computer output. In addition, the book discusses frequently encountered challenges in analyzing biomarker data and how to deal with them, methods for the quality assessment of biomarkers, and biomarker study designs. Covering a broad range of statistical methods that have been used to analyze biomarker data in published research studies, Analysis of Biomarker Data: A Practical Guide also features: A greater emphasis on the application of methods as opposed to the underlying statistical and mathematical theory The use of SAS®, R, and other software throughout to illustrate the presented calculations for each example Numerous exercises based on real-world data as well as solutions to the problems to aid in reader comprehension The principles of good research study design and the methods for assessing the quality of a newly proposed biomarker A companion website that includes a software appendix with multiple types of software and complete data sets from the book’s examples Analysis of Biomarker Data: A Practical Guide is an ideal upper-undergraduate and graduate-level textbook for courses in the biological or environmental sciences. An excellent reference for statisticians who routinely analyze and interpret biomarker data, the book is also useful for researchers who wish to perform their own analyses of biomarker data, such as toxicologists, pharmacologists, epidemiologists, environmental and clinical laboratory scientists, and other professionals in the health and environmental sciences. |
| biomarker validation study design: Improving and Accelerating Therapeutic Development for Nervous System Disorders Institute of Medicine, Board on Health Sciences Policy, Forum on Neuroscience and Nervous System Disorders, 2014-02-06 Improving and Accelerating Therapeutic Development for Nervous System Disorders is the summary of a workshop convened by the IOM Forum on Neuroscience and Nervous System Disorders to examine opportunities to accelerate early phases of drug development for nervous system drug discovery. Workshop participants discussed challenges in neuroscience research for enabling faster entry of potential treatments into first-in-human trials, explored how new and emerging tools and technologies may improve the efficiency of research, and considered mechanisms to facilitate a more effective and efficient development pipeline. There are several challenges to the current drug development pipeline for nervous system disorders. The fundamental etiology and pathophysiology of many nervous system disorders are unknown and the brain is inaccessible to study, making it difficult to develop accurate models. Patient heterogeneity is high, disease pathology can occur years to decades before becoming clinically apparent, and diagnostic and treatment biomarkers are lacking. In addition, the lack of validated targets, limitations related to the predictive validity of animal models - the extent to which the model predicts clinical efficacy - and regulatory barriers can also impede translation and drug development for nervous system disorders. Improving and Accelerating Therapeutic Development for Nervous System Disorders identifies avenues for moving directly from cellular models to human trials, minimizing the need for animal models to test efficacy, and discusses the potential benefits and risks of such an approach. This report is a timely discussion of opportunities to improve early drug development with a focus toward preclinical trials. |
| biomarker validation study design: A National Cancer Clinical Trials System for the 21st Century Institute of Medicine, Board on Health Care Services, Committee on Cancer Clinical Trials and the NCI Cooperative Group Program, 2010-07-08 The National Cancer Institute's (NCI) Clinical Trials Cooperative Group Program has played a key role in developing new and improved cancer therapies. However, the program is falling short of its potential, and the IOM recommends changes that aim to transform the Cooperative Group Program into a dynamic system that efficiently responds to emerging scientific knowledge; involves broad cooperation of stakeholders; and leverages evolving technologies to provide high-quality, practice-changing research. |
| biomarker validation study design: Advancing the Science of Cancer in Latinos Amelie G. Ramirez, Edward J. Trapido, 2019-11-21 This open access book gives an overview of the sessions, panel discussions, and outcomes of the Advancing the Science of Cancer in Latinos conference, held in February 2018 in San Antonio, Texas, USA, and hosted by the Mays Cancer Center and the Institute for Health Promotion Research at UT Health San Antonio. Latinos – the largest, youngest, and fastest-growing minority group in the United States – are expected to face a 142% rise in cancer cases in coming years. Although there has been substantial advancement in cancer prevention, screening, diagnosis, and treatment over the past few decades, addressing Latino cancer health disparities has not nearly kept pace with progress. The diverse and dynamic group of speakers and panelists brought together at the Advancing the Science of Cancer in Latinos conference provided in-depth insights as well as progress and actionable goals for Latino-focused basic science research, clinical best practices, community interventions, and what can be done by way of prevention, screening, diagnosis, and treatment of cancer in Latinos. These insights have been translated into the chapters included in this compendium; the chapters summarize the presentations and include current knowledge in the specific topic areas, identified gaps, and top priority areas for future cancer research in Latinos. Topics included among the chapters: Colorectal cancer disparities in Latinos: Genes vs. Environment Breast cancer risk and mortality in women of Latin American origin Differential cancer risk in Latinos: The role of diet Overcoming barriers for Latinos on cancer clinical trials Es tiempo: Engaging Latinas in cervical cancer research Emerging policies in U.S. health care Advancing the Science of Cancer in Latinos proves to be an indispensable resource offering key insights into actionable targets for basic science research, suggestions for clinical best practices and community interventions, and novel strategies and advocacy opportunities to reduce health disparities in Latino communities. It will find an engaged audience among researchers, academics, physicians and other healthcare professionals, patient advocates, students, and others with an interest in the broad field of Latino cancer. |
| biomarker validation study design: The Evaluation of Surrogate Endpoints Geert Molenberghs, Tomasz Burzykowski, Marc Buyse, 2005-02-28 Covers the latest research on a sensitive and controversial topic in a professional and well researched manner. Provides practical outlook as well as model guidelines and software tools that should be of interest to people who use the software tools described and those who do not. Related title by Co-author Geert Molenbergh has sold more than 3500 copies world wide. Provides dual viewpoints: from scientists in the industry as well as regulatory authorities. |
| biomarker validation study design: Neuroscience Biomarkers and Biosignatures Institute of Medicine, Board on Health Sciences Policy, Forum on Neuroscience and Nervous System Disorders, 2008-01-08 Biomarkers, or biological markers, are quantitative measurements that offer researchers and clinicians valuable insight into diagnosis, treatment and prognosis for many disorders and diseases. A major goal in neuroscience medical research is establishing biomarkers for disorders of the nervous system. Given the promising potential and necessity for neuroscience biomarkers, the Institute of Medicine Forum on Neuroscience and Nervous System Disorders convened a public workshop and released the workshop summary entitled Neuroscience Biomarkers and Biosignatures: Converging Technologies, Emerging Partnerships. The workshop brought together experts from multiple areas to discuss the most promising and practical arenas in neuroscience in which biomarkers will have the greatest impact. The main objective of the workshop was to identify and discuss biomarker targets that are not currently being aggressively pursued but that could have the greatest near-term impact on the rate at which new treatments are brought forward for psychiatric and neurological disorders. |
| biomarker validation study design: Biomarker Analysis in Clinical Trials with R Nusrat Rabbee, 2020-03-11 The world is awash in data. This volume of data will continue to increase. In the pharmaceutical industry, much of this data explosion has happened around biomarker data. Great statisticians are needed to derive understanding from these data. This book will guide you as you begin the journey into communicating, understanding and synthesizing biomarker data. -From the Foreword, Jared Christensen, Vice President, Biostatistics Early Clinical Development, Pfizer, Inc. Biomarker Analysis in Clinical Trials with R offers practical guidance to statisticians in the pharmaceutical industry on how to incorporate biomarker data analysis in clinical trial studies. The book discusses the appropriate statistical methods for evaluating pharmacodynamic, predictive and surrogate biomarkers for delivering increased value in the drug development process. The topic of combining multiple biomarkers to predict drug response using machine learning is covered. Featuring copious reproducible code and examples in R, the book helps students, researchers and biostatisticians get started in tackling the hard problems of designing and analyzing trials with biomarkers. Features: Analysis of pharmacodynamic biomarkers for lending evidence target modulation. Design and analysis of trials with a predictive biomarker. Framework for analyzing surrogate biomarkers. Methods for combining multiple biomarkers to predict treatment response. Offers a biomarker statistical analysis plan. R code, data and models are given for each part: including regression models for survival and longitudinal data, as well as statistical learning models, such as graphical models and penalized regression models. |
| biomarker validation study design: Clinical Trials Tom Brody, 2016-02-19 Clinical Trials, Second Edition, offers those engaged in clinical trial design a valuable and practical guide. This book takes an integrated approach to incorporate biomedical science, laboratory data of human study, endpoint specification, legal and regulatory aspects and much more with the fundamentals of clinical trial design. It provides an overview of the design options along with the specific details of trial design and offers guidance on how to make appropriate choices. Full of numerous examples and now containing actual decisions from FDA reviewers to better inform trial design, the 2nd edition of Clinical Trials is a must-have resource for early and mid-career researchers and clinicians who design and conduct clinical trials. - Contains new and fully revised material on key topics such as biostatistics, biomarkers, orphan drugs, biosimilars, drug regulations in Europe, drug safety, regulatory approval and more - Extensively covers the study schema and related features of study design - Incorporates laboratory data from studies on human patients to provide a concrete tool for understanding the concepts in the design and conduct of clinical trials - Includes decisions made by FDA reviewers when granting approval of a drug as real world learning examples for readers |
| biomarker validation study design: The Statistical Evaluation of Medical Tests for Classification and Prediction Margaret Sullivan Pepe, 2003-03-13 This book describes statistical techniques for the design and evaluation of research studies on medical diagnostic tests, screening tests, biomarkers and new technologies for classification and prediction in medicine. |
| biomarker validation study design: Prognosis Research in Healthcare Richard D. Riley, Danielle van der Windt, Peter Croft, Karel G. M. Moons, 2019-01-17 What is going to happen to me? Most patients ask this question during a clinical encounter with a health professional. As well as learning what problem they have (diagnosis) and what needs to be done about it (treatment), patients want to know about their future health and wellbeing (prognosis). Prognosis research can provide answers to this question and satisfy the need for individuals to understand the possible outcomes of their condition, with and without treatment. Central to modern medical practise, the topic of prognosis is the basis of decision making in healthcare and policy development. It translates basic and clinical science into practical care for patients and populations. Prognosis Research in Healthcare: Concepts, Methods and Impact provides a comprehensive overview of the field of prognosis and prognosis research and gives a global perspective on how prognosis research and prognostic information can improve the outcomes of healthcare. It details how to design, carry out, analyse and report prognosis studies, and how prognostic information can be the basis for tailored, personalised healthcare. In particular, the book discusses how information about the characteristics of people, their health, and environment can be used to predict an individual's future health. Prognosis Research in Healthcare: Concepts, Methods and Impact, addresses all types of prognosis research and provides a practical step-by-step guide to undertaking and interpreting prognosis research studies, ideal for medical students, health researchers, healthcare professionals and methodologists, as well as for guideline and policy makers in healthcare wishing to learn more about the field of prognosis. |
| biomarker validation study design: Generating Evidence for Genomic Diagnostic Test Development Institute of Medicine, Board on Health Sciences Policy, Roundtable on Translating Genomic-Based Research for Health, 2011-07-27 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. |
| biomarker validation study design: Case Studies in Modern Drug Discovery and Development Xianhai Huang, Robert G. Aslanian, 2012-05-29 Learn why some drug discovery and development efforts succeed . . . and others fail Written by international experts in drug discovery and development, this book sets forth carefully researched and analyzed case studies of both successful and failed drug discovery and development efforts, enabling medicinal chemists and pharmaceutical scientists to learn from actual examples. Each case study focuses on a particular drug and therapeutic target, guiding readers through the drug discovery and development process, including drug design rationale, structure-activity relationships, pharmacology, drug metabolism, biology, and clinical studies. Case Studies in Modern Drug Discovery and Development begins with an introductory chapter that puts into perspective the underlying issues facing the pharmaceutical industry and provides insight into future research opportunities. Next, there are fourteen detailed case studies, examining: All phases of drug discovery and development from initial idea to commercialization Some of today's most important and life-saving medications Drugs designed for different therapeutic areas such as cardiovascular disease, infection, inflammation, cancer, metabolic syndrome, and allergies Examples of prodrugs and inhaled drugs Reasons why certain drugs failed to advance to market despite major research investments Each chapter ends with a list of references leading to the primary literature. There are also plenty of tables and illustrations to help readers fully understand key concepts, processes, and technologies. Improving the success rate of the drug discovery and development process is paramount to the pharmaceutical industry. With this book as their guide, readers can learn from both successful and unsuccessful efforts in order to apply tested and proven science and technologies that increase the probability of success for new drug discovery and development projects. |
| biomarker validation study design: Handbook of Biomarkers and Precision Medicine Claudio Carini, Mark Fidock, Alain van Gool, 2019-04-16 The field of Biomarkers and Precision Medicine in drug development is rapidly evolving and this book presents a snapshot of exciting new approaches. By presenting a wide range of biomarker applications, discussed by knowledgeable and experienced scientists, readers will develop an appreciation of the scope and breadth of biomarker knowledge and find examples that will help them in their own work. -Maria Freire, Foundation for the National Institutes of Health Handbook of Biomarkers and Precision Medicine provides comprehensive insights into biomarker discovery and development which has driven the new era of Precision Medicine. A wide variety of renowned experts from government, academia, teaching hospitals, biotechnology and pharmaceutical companies share best practices, examples and exciting new developments. The handbook aims to provide in-depth knowledge to research scientists, students and decision makers engaged in Biomarker and Precision Medicine-centric drug development. Features: Detailed insights into biomarker discovery, validation and diagnostic development with implementation strategies Lessons-learned from successful Precision Medicine case studies A variety of exciting and emerging biomarker technologies The next frontiers and future challenges of biomarkers in Precision Medicine Claudio Carini, Mark Fidock and Alain van Gool are internationally recognized as scientific leaders in Biomarkers and Precision Medicine. They have worked for decades in academia and pharmaceutical industry in EU, USA and Asia. Currently, Dr. Carini is Honorary Faculty at Kings’s College School of Medicine, London, UK. Dr. Fidock is Vice President of Precision Medicine Laboratories at AstraZeneca, Cambridge, UK. Prof.dr. van Gool is Head Translational Metabolic Laboratory at Radboud university medical school, Nijmegen, NL. |
| biomarker validation study design: Biomarker Validation Harald Seitz, Sarah Schumacher, 2015-02-23 Built on a decade of experience with novel molecular diagnostics, this practice-oriented guide shows how to cope with validation issues during all stages of biomarker development, from the first clinical studies to the eventual commercialization of a new diagnostic test. |
| biomarker validation study design: Statistical Methods in Biomarker and Early Clinical Development Liang Fang, Cheng Su, 2019-12-26 This contributed volume offers a much-needed overview of the statistical methods in early clinical drug and biomarker development. Chapters are written by expert statisticians with extensive experience in the pharmaceutical industry and regulatory agencies. Because of this, the data presented is often accompanied by real world case studies, which will help make examples more tangible for readers. The many applications of statistics in drug development are covered in detail, making this volume a must-have reference. Biomarker development and early clinical development are the two critical areas on which the book focuses. By having the two sections of the book dedicated to each of these topics, readers will have a more complete understanding of how applying statistical methods to early drug development can help identify the right drug for the right patient at the right dose. Also presented are exciting applications of machine learning and statistical modeling, along with innovative methods and state-of-the-art advances, making this a timely and practical resource. This volume is ideal for statisticians, researchers, and professionals interested in pharmaceutical research and development. Readers should be familiar with the fundamentals of statistics and clinical trials. |
| biomarker validation study design: 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. |
| biomarker validation study design: Drug Discovery in Cancer Epigenetics Gerda Egger, Paola Arimondo, 2015-11-19 Drug Discovery in Cancer Epigenetics is a practical resource for scientists involved in the discovery, testing, and development of epigenetic cancer drugs. Epigenetic modifications can have significant implications for translational science as biomarkers for diagnosis, prognosis or therapy prediction. Most importantly, epigenetic modifications are reversible and epigenetic players are found mutated in different cancers; therefore, they provide attractive therapeutic targets. There has been great interest in developing and testing epigenetic drugs, which inhibit DNA methyltransferases, histone modifying enzymes or chromatin reader proteins. The first few drugs are already FDA approved and have made their way into clinical settings. This book provides a comprehensive summary of the epigenetic drugs currently available and aims to increase awareness in this area to foster more rapid translation of epigenetic drugs into the clinic. - Highlights the potential of epigenetic alterations in cancer for drug development - Covers the tools and methods for epigenetic drug discovery, preclinical and clinical testing, and clinical implications of epigenetic therapy - Provides important information regarding putative epigenetic targets, epigenetic technologies, networks and consortia for epigenetic drug discovery and routes for translation |
| biomarker validation study design: Precision Medicine in Oncology Bulent Aydogan, James A. Radosevich, 2020-11-02 A FRESH EXAMINATION OF PRECISION MEDICINE'S INCREASINGLY PROMINENT ROLE IN THE FIELD OF ONCOLOGY Precision medicine takes into account each patient's specific characteristics and requirements to arrive at treatment plans that are optimized towards the best possible outcome. As the field of oncology continues to advance, this tailored approach is becoming more and more prevalent, channelling data on genomics, proteomics, metabolomics and other areas into new and innovative methods of practice. Precision Medicine in Oncology draws together the essential research driving the field forward, providing oncology clinicians and trainees alike with an illuminating overview of the technology and thinking behind the breakthroughs currently being made. Topics covered include: Biologically-guided radiation therapy Informatics for precision medicine Molecular imaging Biomarkers for treatment assessment Big data Nanoplatforms Casting a spotlight on this emerging knowledge base and its impact upon the management of tumors, Precision Medicine in Oncology opens up new possibilities and ways of working not only for oncologists, but also for molecular biologists, radiologists, medical geneticists, and others. |
| biomarker validation study design: Neuroscience Trials of the Future National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Health Sciences Policy, Forum on Neuroscience and Nervous System Disorders, 2016-11-07 On March 3-4, 2016, the National Academies of Sciences, Engineering, and Medicine's Forum on Neuroscience and Nervous System Disorders held a workshop in Washington, DC, bringing together key stakeholders to discuss opportunities for improving the integrity, efficiency, and validity of clinical trials for nervous system disorders. Participants in the workshop represented a range of diverse perspectives, including individuals not normally associated with traditional clinical trials. The purpose of this workshop was to generate discussion about not only what is feasible now, but what may be possible with the implementation of cutting-edge technologies in the future. |
| biomarker validation study design: Regression Modeling Strategies Frank E. Harrell, 2013-03-09 Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with too many variables to analyze and not enough observations, and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve safe data mining. |
| biomarker validation study design: Biomarkers in Inflammatory Bowel Diseases Nik Sheng Ding, Peter De Cruz, 2019-05-13 This book provides a comprehensive and complete overview of biomarkers in clinical practice for inflammatory bowel disease (IBD) bringing together the literature in a clear and concise manner. The book bridges the gap between growing knowledge at the bench and current and future applications of biomarkers in clinical practice. The central structure of the book focuses on prognostic and predictive biomarkers in IBD with an emphasis on the fields of research and scientific techniques (genomics, proteomics and metabonomics) that have led to biomarker discovery and places these biomarkers within a clinical context to help understand their utility in clinical practice. This book will be of use to clinicians who have an interest in using biomarkers in clinical practice as well as clinician researchers and scientists involved in the biomarker research pipeline. The author team comprises experts from around the world in order to bring together the literature in an effort to inform clinicians and researchers about the current state-of-the art in biomarker discovery. It is intended to assist future research efforts and indicate how biomarkers might be best applied to clinical practice both at present and in the future. |
| biomarker validation study design: Improving the Quality of Cancer Clinical Trials Institute of Medicine, National Cancer Policy Forum, 2008-06-12 Scientists and clinicians seek a new paradigm that could improve the efficiency, cost-effectiveness, and overall success rate of cancer clinical trials, while maintaining the highest standards of quality. To explore innovative paradigms for cancer clinical trials and other ways to improve their quality, the National Cancer Policy Forum held a workshop, Improving the Quality of Cancer Clinical Trials, in Washington, DC. The main goals of the workshop were to examine new approaches to clinical trial design and execution that would: (1) better inform decisions and plans of those responsible for developing new cancer therapies (2) more rapidly move new diagnostic tests and treatments toward regulatory approval and use in the clinic (3) be less costly than current trials The resulting workshop summary will serve as input to the deliberations of an Institute of Medicine committee that will develop consensus-based recommendations for moving the field of cancer clinical trials forward. |
| biomarker validation study design: Statistical Analysis with Missing Data Roderick J. A. Little, Donald B. Rubin, 2019-03-21 An up-to-date, comprehensive treatment of a classic text on missing data in statistics The topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism, and then they apply the theory to a wide range of important missing data problems. Statistical Analysis with Missing Data, Third Edition starts by introducing readers to the subject and approaches toward solving it. It looks at the patterns and mechanisms that create the missing data, as well as a taxonomy of missing data. It then goes on to examine missing data in experiments, before discussing complete-case and available-case analysis, including weighting methods. The new edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics. An updated “classic” written by renowned authorities on the subject Features over 150 exercises (including many new ones) Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methods Revises previous topics based on past student feedback and class experience Contains an updated and expanded bibliography The authors were awarded The Karl Pearson Prize in 2017 by the International Statistical Institute, for a research contribution that has had profound influence on statistical theory, methodology or applications. Their work has been no less than defining and transforming. (ISI) Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry. |
| biomarker validation study design: Decision Making in Health and Medicine M. G. Myriam Hunink, Milton C. Weinstein, Eve Wittenberg, 2014-10-16 A guide for everyone involved in medical decision making to plot a clear course through complex and conflicting benefits and risks. |
| biomarker validation study design: Evaluation of diagnostic systems John Swets, 2012-12-02 Evaluation of Diagnostic Systems: Methods from Signal Detection Theory addresses the many issues that arise in evaluating the performance of a diagnostic system, across the wide range of settings in which such systems are used. These settings include clinical medicine, industrial quality control, environmental monitoring and investigation, machine and metals inspection, military monitoring, information retrieval, and crime investigation. The book is divided into three parts encompassing 11 chapters that emphasize the interpretation of diagnostic visual images by human observers. The first part of the book describes quantitative methods for measuring the accuracy of a system and the statistical techniques for drawing inferences from performance tests. The subsequent part covers study design and includes a detailed description of the form and conduct of an image-interpretation test. The concluding part examines the case study of a medical imaging system that serves as an example of both simple and complex applications. In this part, three mammographic modalities are used: industrial film radiography, low-dose film radiography, and xeroradiography. The case study focuses on the overall reliability of accuracy indices made by its main components, that is, the variabilities across cases, across readers, and within individual readers. The supplementary texts provide study protocols, a computer program for processing test results, and an extensive list of references that will assist the reader in applying those evaluative methods to diagnostic systems in any setting. This book is of value to scientists and engineers, as well as to applied, quantitative, or experimental psychologists who are engaged in the study of the human processes of discrimination and decision making in either perceptual or cognitive tasks. |
| biomarker validation study design: The Handbook of Biomarkers Kewal K. Jain, 2010-02-06 Of the thousands of biomarkers that are currently being discovered, relatively few are being validated for further applications, and the potential of a biomarker can be quite difficult to evaluate. To aid in this imperative research, Dr. Kewal K. Jain’s Handbook of Biomarkers thoroughly describes many different types of biomarkers and their discovery using various -omics technologies, such as proteomics and metabolomics, along with the background information needed for the evaluation of biomarkers as well as the essential procedures for their validation and use in clinical trials. With biomarkers described first according to technologies and then according to various diseases, this detailed book features the key correlations between diseases and classifications of biomarkers, which provides the reader with a guide to sort out current and future biomarkers. Comprehensive and cutting-edge, The Handbook of Biomarkers serves as a vital guide to furthering our understanding of biomarkers, which, by facilitating the combination of therapeutics with diagnostics, promise to play an important role in the development of personalized medicine, one of the most important emerging trends in healthcare today. |
| biomarker validation study design: Adaptive Design Methods in Clinical Trials Shein-Chung Chow, Mark Chang, 2011-12-01 With new statistical and scientific issues arising in adaptive clinical trial design, including the U.S. FDA's recent draft guidance, a new edition of one of the first books on the topic is needed. Adaptive Design Methods in Clinical Trials, Second Edition reflects recent developments and regulatory positions on the use of adaptive designs in clini |
| biomarker validation study design: Early Diagnosis of Alzheimer's Disease Leonard F. M. Scinto, Kirk R. Daffner, 2000-02-09 The three major approaches to diagnosis of AD -- radiological, biological, and neurophysiological -- are discussed in detail with chapters highlighting the most promising technologies within these approaches. The leading authors, all of whom are intimately involved with these emerging technologies, have developed this as an essential reference for neuropathologists, clinicians and researchers of Alzheimer's disease. |
| biomarker validation study design: Samples:From the Patient to the Laboratory Walter G. Guder, Sheshadri Narayanan, Hermann Wisser, Bernd Zawta, 2008-01-08 This forth updated edition contains the latest developments in analytical techniques. An international team of authors summarizes the information on biological influences, analytical interferences and on the variables affecting the collection, transport and storage as well as preparation of samples. They cover age, gender, race, pregnancy, diet, exercise and altitude, plus the effects of stimulants and drugs. National and international standards are described for sampling procedures, transport, sample identification and all safety aspects, while quality assurance procedures are shown for total laboratory management. In addition, the authors provide a glossary as well as a separate list of analytes containing the available data on reference intervals, biological half-life times, stability and influence and interference factors. For everyone involved in patient care and using or performing laboratory tests. |
| biomarker validation study design: Cancer Theranostics Xiaoyuan Chen, Stephen Wong, 2014-03-20 Aiding researchers seeking to eliminate multi-step procedures, reduce delays in treatment and ease patient care, Cancer Theranostics reviews, assesses, and makes pertinent clinical recommendations on the integration of comprehensive in vitro diagnostics, in vivo molecular imaging, and individualized treatments towards the personalization of cancer treatment. Cancer Theranostics describes the identification of novel biomarkers to advance molecular diagnostics of cancer. The book encompasses new molecular imaging probes and techniques for early detection of cancer, and describes molecular imaging-guided cancer therapy. Discussion also includes nanoplatforms incorporating both cancer imaging and therapeutic components, as well as clinical translation and future perspectives. - Supports elimination of multi-step approaches and reduces delays in treatments through combinatorial diagnosis and therapy - Fully assesses cancer theranostics across the emergent field, with discussion of biomarkers, molecular imaging, imaging guided therapy, nanotechnology, and personalized medicine - Content bridges laboratory, clinic, and biotechnology industries to advance biomedical science and improve patient management |
| biomarker validation study design: 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. |
Design and Analysis of Biomarker Studies - UC Davis Health
Objective: to develop a metabolomic signature as a prognostic biomarker for ADPKD. Large confirmatory studies with pre‐stated hypotheses and precise quantification of the magnitude of …
M10 BIOANALYTICAL METHOD VALIDATION AND STUDY …
This guidance is intended to provide recommendations for the validation of bioanalytical methods for chemical and biological drug quantification and their application in the analysis of study...
A biomarker assay validation approach tailored to the context …
biomarker assay validations on multiple bioanalytical platforms. ן A series of strategy and platform-specific technical documents enables a consistent approach to fit- or-purpose biomarker …
Biomarker Discovery and Validation: Statistical Considerations
Biomarker discov-ery and validation are essential steps in establishing biomarkers in all applications across the disease course. In this article, we discuss the best practices for …
Biomarker Validation Study Design (PDF) - old.icapgen.org
Haleem J. Issaq,Timothy D. Veenstra Biomarker Validation Study Design: Analysis of Biomarker Data Stephen W. Looney,Joseph L. Hagan,2015-01-28 A how to guide for applying statistical …
Biomarker Validation: Why, which, and how?
Validation is a continually ongoing, iterative, process by which one improves understanding of biomarker characteristics through experimental study. A priori determination of what evidence …
Tutorial: best practices and considerations for mass ... - Nature
In this tutorial, we describe key points that should be considered for performing biomarker discovery experiments based on liquid-chromatography–mass-spectrometry analysis of human …
Analytical method validation for biomarkers as a drug …
The Biomarker Analytical Method Validation Study Group is a research group in Japan comprising industry and regulatory experts. Group members discussed and prepared this ‘points to …
Bioanalytical Method Validation Guidance for Industry
Validated analytical methods for the quantitative evaluation of analytes (i.e., drugs, including biologic products, and their metabolites) and biomarkers in a given biological matrix (e.g....
Biomarker based clinical trial design
A predictive biomarker is a biological measurement that indicates whether the patient is likely to respond to the particular drug. It is distinguished from a prognostic biomarker which may …
Analytical Considerations: Discovery or Validation of …
Experimental design in biomarker discovery studies can be minimally defined as the set of plans, protocols, and procedures, which aims to explain the variation and minimize bias while …
Planning clinically relevant biomarker validation studies using …
A biomarker validation study justified by an artic-ulated patient-relevant quantitative goal will have suit-able design and sample sizes. After completing the study, a reader can compare that …
Biomarker Validation Study Design (PDF) - old.icapgen.org
Approaches to Biomarker Discovery demonstrates how to leverage biomarkers to improve accuracy and reduce errors in research Disease biomarker discovery is one of the most vibrant …
Bioanalytical Method Validation for Biomarkers Guidance
Jan 21, 2025 · This guidance helps sponsors of investigational new drug applications (INDs) and applicants of new drug applications (NDAs), biologics license applications (BLAs), and NDA …
Biomarker Assay Development, Qualification, and Validati
Data analysis models and format for reporting results. Validation and optimization criteria using statistical experimental design tools. Recovery, accuracy, and precision expected at the limits …
Biomarker Validation Study Design (Download Only)
Biomarker Validation Study Design: Analysis of Biomarker Data Stephen W. Looney,Joseph L. Hagan,2015-01-28 A how to guide for applying statistical methods to biomarker data analysis …
Validation of Diagnostic Classifiers Based on Profile Biomarkers
With proper cross-validation, the model must be developed from scratch for each leave-one-out training set. This means that feature selection must be repeated for each leave-one-out training …
Efficiency of Biomarker-Driven Clinical Trial Designs
Mar 4, 2024 · Implementation of precision medicine requires biomarker-driven randomized clinical trials (RCTs) to generate reliable evidence-based assessment of all relevant components of …
Design, cohort profile and comparison of the KTD-Innov …
The KTD-Innov clinical study is a prospective multicenter biomarker validation study aiming to determine the clini-cal utility of various precision diagnostic biomarkers and intragraft gene …
BIOMARKER STUDY Evaluation Guidelines - National …
BIOMARKER STUDY Evaluation Guidelines Purpose and Background damental design of a clinical trial. The objective of this initiative is to ensure that the most important biomarker …
Design and Analysis of Biomarker Studies - UC Davis Health
Objective: to develop a metabolomic signature as a prognostic biomarker for ADPKD. Large confirmatory studies with pre‐stated hypotheses and precise quantification of the magnitude of …
M10 BIOANALYTICAL METHOD VALIDATION AND STUDY …
This guidance is intended to provide recommendations for the validation of bioanalytical methods for chemical and biological drug quantification and their application in the analysis of study...
A biomarker assay validation approach tailored to the context …
biomarker assay validations on multiple bioanalytical platforms. ן A series of strategy and platform-specific technical documents enables a consistent approach to fit- or-purpose biomarker …
Biomarker Discovery and Validation: Statistical Considerations
Biomarker discov-ery and validation are essential steps in establishing biomarkers in all applications across the disease course. In this article, we discuss the best practices for …
Biomarker Validation Study Design (PDF) - old.icapgen.org
Haleem J. Issaq,Timothy D. Veenstra Biomarker Validation Study Design: Analysis of Biomarker Data Stephen W. Looney,Joseph L. Hagan,2015-01-28 A how to guide for applying statistical …
Biomarker Validation: Why, which, and how?
Validation is a continually ongoing, iterative, process by which one improves understanding of biomarker characteristics through experimental study. A priori determination of what evidence …
Tutorial: best practices and considerations for mass ... - Nature
In this tutorial, we describe key points that should be considered for performing biomarker discovery experiments based on liquid-chromatography–mass-spectrometry analysis of …
Analytical method validation for biomarkers as a drug …
The Biomarker Analytical Method Validation Study Group is a research group in Japan comprising industry and regulatory experts. Group members discussed and prepared this ‘points to …
Bioanalytical Method Validation Guidance for Industry
Validated analytical methods for the quantitative evaluation of analytes (i.e., drugs, including biologic products, and their metabolites) and biomarkers in a given biological matrix (e.g....
Biomarker based clinical trial design
A predictive biomarker is a biological measurement that indicates whether the patient is likely to respond to the particular drug. It is distinguished from a prognostic biomarker which may …
Analytical Considerations: Discovery or Validation of …
Experimental design in biomarker discovery studies can be minimally defined as the set of plans, protocols, and procedures, which aims to explain the variation and minimize bias while …
Planning clinically relevant biomarker validation studies using …
A biomarker validation study justified by an artic-ulated patient-relevant quantitative goal will have suit-able design and sample sizes. After completing the study, a reader can compare that …
Biomarker Validation Study Design (PDF) - old.icapgen.org
Approaches to Biomarker Discovery demonstrates how to leverage biomarkers to improve accuracy and reduce errors in research Disease biomarker discovery is one of the most …
Bioanalytical Method Validation for Biomarkers Guidance
Jan 21, 2025 · This guidance helps sponsors of investigational new drug applications (INDs) and applicants of new drug applications (NDAs), biologics license applications (BLAs), and NDA …
Biomarker Assay Development, Qualification, and Validati
Data analysis models and format for reporting results. Validation and optimization criteria using statistical experimental design tools. Recovery, accuracy, and precision expected at the limits …
Biomarker Validation Study Design (Download Only)
Biomarker Validation Study Design: Analysis of Biomarker Data Stephen W. Looney,Joseph L. Hagan,2015-01-28 A how to guide for applying statistical methods to biomarker data analysis …
Validation of Diagnostic Classifiers Based on Profile Biomarkers
With proper cross-validation, the model must be developed from scratch for each leave-one-out training set. This means that feature selection must be repeated for each leave-one-out …
Efficiency of Biomarker-Driven Clinical Trial Designs
Mar 4, 2024 · Implementation of precision medicine requires biomarker-driven randomized clinical trials (RCTs) to generate reliable evidence-based assessment of all relevant components of …
Design, cohort profile and comparison of the KTD-Innov …
The KTD-Innov clinical study is a prospective multicenter biomarker validation study aiming to determine the clini-cal utility of various precision diagnostic biomarkers and intragraft gene …
