Author: Geert Molenberghs,Garrett Fitzmaurice,Michael G. Kenward,Anastasios Tsiatis,Geert Verbeke
Publisher: CRC Press
Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research. Divided into six parts, the handbook begins by establishing notation and terminology. It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods. The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative, routinely non-verifiable assumptions about the missing data process. The final part discusses special topics, such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part, an introduction provides useful background material and an overview to set the stage for subsequent chapters. Covering both established and emerging methodologies for missing data, this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.
National Research Council,Division of Behavioral and Social Sciences and Education,Committee on National Statistics,Panel on Handling Missing Data in Clinical Trials
Author: National Research Council,Division of Behavioral and Social Sciences and Education,Committee on National Statistics,Panel on Handling Missing Data in Clinical Trials
Publisher: National Academies Press
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.
This book provides practical guidance for statisticians,clinicians, and researchers involved in clinical trials in thebiopharmaceutical industry, medical and public healthorganisations. Academics and students needing an introduction tohandling missing data will also find this book invaluable. The authors describe how missing data can affect the outcome andcredibility of a clinical trial, show by examples how a clinicalteam can work to prevent missing data, and present the reader withapproaches to address missing data effectively. The book is illustrated throughout with realistic case studies andworked examples, and presents clear and concise guidelines toenable good planning for missing data. The authors show how tohandle missing data in a way that is transparent and easy tounderstand for clinicians, regulators and patients. Newdevelopments are presented to improve the choice and implementationof primary and sensitivity analyses for missing data. Many SAS codeexamples are included – the reader is given a toolbox forimplementing analyses under a variety of assumptions.
Author: Mounir Mesbah,Bernard F. Cole,Mei-Ling Ting Lee
Publisher: Springer Science & Business Media
The volume presents a broad spectrum of papers which illustrates a range of current research related to the theory, methods and applications of health related quality of life (HRQoL) as well as the interdisciplinary nature of this work.
More and more frequently, clinical trials include the evaluation of Health-Related Quality of Life (HRQoL), yet many investigators remain unaware of the unique measurement and analysis issues associated with the assessment of HRQoL. At the end of a study, clinicians and statisticians often face challenging and sometimes insurmountable analytic problems. Design and Analysis of Quality of Life Studies in Clinical Trials details these issues and presents a range of solutions. Written from the author's extensive experience in the field, it focuses on the very specific features of QoL data: its longitudinal nature, multidimensionality, and the problem of missing data. The author uses three real clinical trials throughout her discussions to illustrate practical implementation of the strategies and analytic methods presented. As Quality of Life becomes an increasingly important aspect of clinical trials, it becomes essential for clinicians, statisticians, and designers of these studies to understand and meet the challenges this kind of data present. In this book, SAS and S-PLUS programs, checklists, numerous figures, and a clear, concise presentation combine to provide readers with the tools and skills they need to successfully design, conduct, analyze, and report their own studies.
M. Marlyne Kilbey,Khursheed Asghar,National Institute on Drug Abuse
Fully updated, this revised edition describes the statistical aspects of both the design and analysis of trials, with particular emphasis on the more recent methods of analysis.About 8000 clinical trials are undertaken annually in all areas of medicine, from the treatment of acne to the prevention of cancer. Correct interpretation of the data from such trials depends largely on adequate design and on performing the appropriate statistical analyses. This book provides a useful guide to medical statisticians and others faced with the often difficult problems of designing and analysing clinical trials.
Douglas E. Faries,Robert Obenchain,Josep Maria Haro,Andrew C. Leon
Author: Douglas E. Faries,Robert Obenchain,Josep Maria Haro,Andrew C. Leon
Publisher: SAS Institute
This book guides researchers in performing and presenting high-quality analyses of all kinds of non-randomized studies, including analyses of observational studies, claims database analyses, assessment of registry data, survey data, pharmaco-economic data, and many more applications. The text is sufficiently detailed to provide not only general guidance, but to help the researcher through all of the standard issues that arise in such analyses. Just enough theory is included to allow the reader to understand the pros and cons of alternative approaches and when to use each method. The numerous contributors to this book illustrate, via real-world numerical examples and SAS code, appropriate implementations of alternative methods. The end result is that researchers will learn how to present high-quality and transparent analyses that will lead to fair and objective decisions from observational data. This book is part of the SAS Press program.
Howard M. Fillit,Kenneth Rockwood,Kenneth Woodhouse
Author: Howard M. Fillit,Kenneth Rockwood,Kenneth Woodhouse
Publisher: Elsevier Health Sciences
Popular with generations of practitioners, Brocklehurst's Textbook of Geriatric Medicine and Gerontology has been the definitive reference of choice in the field of geriatric care. The new 7th Edition, by Howard M. Fillit, MD, Kenneth Rockwood, MD, and Kenneth Woodhouse, carries on this tradition with an increased clinical focus and updated coverage to help you meet the unique challenges posed by this growing patient population. Consistent discussions of clinical manifestations, diagnosis, prevention, treatment, and more make reference quick and easy, while over 255 illustrations compliment the text to help you find what you need on a given condition. Examples of the latest imaging studies depict the effects of aging on the brain, and new algorithms further streamline decision making. Emphasizes the clinical relevance of the latest scientific findings to help you easily apply the material to everyday practice. Features consistent discussions of clinical manifestations, diagnosis, prevention, treatment, and more that make reference quick and easy. Includes over 255 illustrations—including algorithms, photographs, and tables—that compliment the text to help you find what you need on a given condition. Provides summary boxes at the end of each chapter that highlight important points. Features the work of an expert author team, now led by Dr. Howard M. Fillit who provides an American perspective to complement the book’s traditional wealth of British expertise. Includes an expanded use of algorithms to streamline decision making. Presents more color images in the section on aging skin, offering a real-life perspective of conditions for enhanced diagnostic accuracy. Includes examples of the latest imaging studies to help you detect and classify changes to the brain during aging. Offers Grade A evidence-based references keyed to the relevant text.
Written with medical statisticians and medical researchers in mind, this intermediate-level reference explores the use of SAS for analyzing medical data. Applied Medical Statistics Using SAS covers the whole range of modern statistical methods used in the analysis of medical data, including regression, analysis of variance and covariance, longitudinal and survival data analysis, missing data, generalized additive models (GAMs), and Bayesian methods. The book focuses on performing these analyses using SAS, the software package of choice for those analysing medical data. Features Covers the planning stage of medical studies in detail; several chapters contain details of sample size estimation Illustrates methods of randomisation that might be employed for clinical trials Covers topics that have become of great importance in the 21st century, including Bayesian methods and multiple imputation Its breadth and depth, coupled with the inclusion of all the SAS code, make this book ideal for practitioners as well as for a graduate class in biostatistics or public health. Complete data sets, all the SAS code, and complete outputs can be found on an associated website: http://support.sas.com/amsus
Clinical trials have become essential research tools for evaluating the benefits and risks of new interventions for the treatment and prevention of diseases, from cardiovascular disease to cancer to AIDS. Based on the authors’ collective experiences in this field, Introduction to Statistical Methods for Clinical Trials presents various statistical topics relevant to the design, monitoring, and analysis of a clinical trial. After reviewing the history, ethics, protocol, and regulatory issues of clinical trials, the book provides guidelines for formulating primary and secondary questions and translating clinical questions into statistical ones. It examines designs used in clinical trials, presents methods for determining sample size, and introduces constrained randomization procedures. The authors also discuss how various types of data must be collected to answer key questions in a trial. In addition, they explore common analysis methods, describe statistical methods that determine what an emerging trend represents, and present issues that arise in the analysis of data. The book concludes with suggestions for reporting trial results that are consistent with universal guidelines recommended by medical journals. Developed from a course taught at the University of Wisconsin for the past 25 years, this textbook provides a solid understanding of the statistical approaches used in the design, conduct, and analysis of clinical trials.
Erstmals wird in diesem Buch ein neues, minimal-invasives Behandlungsverfahren beschrieben, das bei osteoporotischen, traumatischen oder tumorösen Veränderungen der Wirbelsäule indiziert ist. Die Technik wird in einfachen Schritten erklärt, um einen breiten, interdisziplinären Interessentenkreis zu erreichen. Da der Schwerpunkt der Technik die Osteoporose betrifft, ist auch ein spezielles Kapitel der unbedingt notwendigen Begleitmedikation gewidmet. Die neue Operationstechnik wird auch mit Alternativen zur Kyphoplastie verglichen. Ferner wird ein neuartiges Nachbehandlungskonzept speziell für die dargestellte Technik eingeführt und erklärt. Abschließend werden Listen über Referenzzentren und Abrechnungsziffern für die Krankenkassen für die deutschsprachigen Länder angeführt. Die Autoren sind seit Jahren Instruktoren für die beschriebene Operationstechnik und haben in den letzten Jahren diese neuen Methoden an verschiedenen Krankenhäusern und Universitäten in Europa gelehrt und durchgeführt.
Author: Marc C. Hochberg,Alan J. Silman,Josef S. Smolen,Michael E. Weinblatt,Michael H. Weisman
Publisher: Elsevier Health Sciences
First Prize, Orthopaedics and Rheumatology, BMA Awards 2009 This state-of-the-art reference provides current insights into the etiology, diagnosis and management of rheumatoid arthritis. Leading international authorities in RA examine all of the latest scientific and clinical developments in understanding and managing this challenging disease, including new concepts in pathogenesis, epidemiology, risk factors, imaging, clinical outcomes and treatment. It’s the guidance you need to offer optimal care to your patients with rheumatoid arthritis. Presents the work of leading international experts in rheumatoid arthritis for guidance you can trust. Provides the very latest understanding of the pathogenesis of rheumatoid arthritis, including molecular pathways/mechanisms, and genetic and environmental factors that instigate and drive the disease. Includes comprehensive coverage of clinical features of rheumatoid arthritis including articular, peri-articular and extra-articular manifestations, comorbidities, and outcome measures—disease activity, joint assessment, imaging, and more—for expert diagnosis and monitoring of disease progression. Examines evidence-based treatment options—including traditional and biologic DMARDs and combination therapies—as well as promising therapies on the horizon, placing up-to-date guidance on disease modifying or disease controlling agents at your fingertips.
Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include: descriptive methods for delineating trends over time linear mixed regression models with both fixed and random effects covariance pattern models on correlated errors generalized estimating equations nonlinear regression models for categorical repeated measurements techniques for analyzing longitudinal data with non-ignorable missing observations Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data. Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists. From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.
Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models. Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods. Visit http://www.ats.ucla.edu/stat/examples/alda.htm for: · Downloadable data sets · Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more · Additional material for data analysis