Analyzing Health Data in R for SAS Users is aimed at helping health data analysts who use SAS accomplish some of the same tasks in R. It is targeted to public health students and professionals who have a background in biostatistics and SAS software, but are new to R. For professors, it is useful as a textbook for a descriptive or regression modeling class, as it uses a publicly-available dataset for examples, and provides exercises at the end of each chapter. For students and public health professionals, not only is it a gentle introduction to R, but it can serve as a guide to developing the results for a research report using R software. Features: Gives examples in both SAS and R Demonstrates descriptive statistics as well as linear and logistic regression Provides exercise questions and answers at the end of each chapter Uses examples from the publicly available dataset, Behavioral Risk Factor Surveillance System (BRFSS) 2014 data Guides the reader on producing a health analysis that could be published as a research report Gives an example of hypothesis-driven data analysis Provides examples of plots with a color insert

Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials is the first book focused on the application of generalized linear mixed models and its related models in the statistical design and analysis of repeated measures from randomized controlled trials. The author introduces a new repeated measures design called S:T design combined with mixed models as a practical and useful framework of parallel group RCT design because of easy handling of missing data and sample size reduction. The book emphasizes practical, rather than theoretical, aspects of statistical analyses and the interpretation of results. It includes chapters in which the author describes some old-fashioned analysis designs that have been in the literature and compares the results with those obtained from the corresponding mixed models. The book will be of interest to biostatisticians, researchers, and graduate students in the medical and health sciences who are involved in clinical trials. Author Website:Data sets and programs used in the book are available at http://www.medstat.jp/downloadrepeatedcrc.html

Comparative effectiveness research (CER) is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care (IOM 2009). CER is conducted to develop evidence that will aid patients, clinicians, purchasers, and health policy makers in making informed decisions at both the individual and population levels. CER encompasses a very broad range of types of studies-experimental, observational, prospective, retrospective, and research synthesis. This volume covers the main areas of quantitative methodology for the design and analysis of CER studies. The volume has four major sections-causal inference; clinical trials; research synthesis; and specialized topics. The audience includes CER methodologists, quantitative-trained researchers interested in CER, and graduate students in statistics, epidemiology, and health services and outcomes research. The book assumes a masters-level course in regression analysis and familiarity with clinical research.

Cost-effectiveness analysis is becoming an increasingly important tool for decision making in the health systems. Cost-Effectiveness of Medical Treatments formulates the cost-effectiveness analysis as a statistical decision problem, identifies the sources of uncertainty of the problem, and gives an overview of the frequentist and Bayesian statistical approaches for decision making. Basic notions on decision theory such as space of decisions, space of nature, utility function of a decision and optimal decisions, are explained in detail using easy to read mathematics. Features Focuses on cost-effectiveness analysis as a statistical decision problem and applies the well-established optimal statistical decision methodology. Discusses utility functions for cost-effectiveness analysis. Enlarges the class of models typically used in cost-effectiveness analysis with the incorporation of linear models to account for covariates of the patients. This permits the formulation of the group (or subgroup) theory. Provides Bayesian procedures to account for model uncertainty in variable selection for linear models and in clustering for models for heterogeneous data. Model uncertainty in cost-effectiveness analysis has not been considered in the literature. Illustrates examples with real data. In order to facilitate the practical implementation of real datasets, provides the codes in Mathematica for the proposed methodology. The motivation for the book is to make the achievements in cost-effectiveness analysis accessible to health providers, who need to make optimal decisions, to the practitioners and to the students of health sciences. Elias Moreno is Professor of Statistics and Operational Research at the University of Granada, Spain, Corresponding Member of the Royal Academy of Sciences of Spain, and elect member of ISI. Francisco Jose Vazquez-Polo is Professor of Mathematics and Bayesian Methods at the University of Las Palmas de Gran Canaria, and Head of the Department of Quantitative Methods. Miguel Angel Negrin is Senior Lecturer in the Department of Quantitative Methods at the ULPGC. His main research topics are Bayesian methods applied to Health Economics, economic evaluation and cost-effectiveness analysis, meta-analysis and equity in the provision of healthcare services.

Develop Effective Immunogenicity Risk Mitigation Strategies Immunogenicity assessment is a prerequisite for the successful development of biopharmaceuticals, including safety and efficacy evaluation. Using advanced statistical methods in the study design and analysis stages is therefore essential to immunogenicity risk assessment and mitigation strategies. Statistical Methods for Immunogenicity Assessment provides a single source of information on statistical concepts, principles, methods, and strategies for detection, quantification, assessment, and control of immunogenicity. The book first gives an overview of the impact of immunogenicity on biopharmaceutical development, regulatory requirements, and statistical methods and strategies used for immunogenicity detection, quantification, and risk assessment and mitigation. It then covers anti-drug antibody (ADA) assay development, optimization, validation, and transfer as well as the analysis of cut point, a key assay performance parameter in ADA assay development and validation. The authors illustrate how to apply statistical modeling approaches to establish associations between ADA and clinical outcomes, predict immunogenicity risk, and develop risk mitigation strategies. They also present various strategies for immunogenicity risk control. The book concludes with an explanation of the computer codes and algorithms of the statistical methods. A critical issue in the development of biologics, immunogenicity can cause early termination or limited use of the products if not managed well. This book shows how to use robust statistical methods for detecting, quantifying, assessing, and mitigating immunogenicity risk. It is an invaluable resource for anyone involved in immunogenicity risk assessment and control in both non-clinical and clinical biopharmaceutical development.

This book focuses on analytical similarity assessment in biosimilar product development following the FDA's recommended stepwise approach for obtaining totality-of-the-evidence for approval of biosimilar products. It covers concepts such as the tiered approach for assessment of similarity of critical quality attributes in the manufacturing process of biosimilar products, models/methods like the statistical model for classification of critical quality attributes, equivalence tests for critical quality attributes in Tier 1 and the corresponding sample size requirements, current issues, and recent developments in analytical similarity assessment.

Data Analysis with Competing Risks and Intermediate States explains when and how to use models and techniques for the analysis of competing risks and intermediate states. It covers the most recent insights on estimation techniques and discusses in detail how to interpret the obtained results. After introducing example studies from the biomedical and epidemiological fields, the book formally defines the concepts that play a role in analyses with competing risks and intermediate states. It addresses nonparametric estimation of the relevant quantities. The book then shows how to use a stacked data set that offers great flexibility in the modeling of covariable effects on the transition rates between states. It also describes three ways to quantify effects on the cumulative scale. Each chapter includes standard exercises that reflect on the concepts presented, a section on software that explains options in SAS and Stata and the functionality in the R program, and computer practicals that allow readers to practice with the techniques using an existing data set of bone marrow transplant patients. The book's website provides the R code for the computer practicals along with other material. For researchers with some experience in the analysis of standard time-to-event data, this practical and thorough treatment extends their knowledge and skills to the competing risks and multi-state settings. Researchers from other fields can also easily translate individuals and diseases to units and phenomena from their own areas.

Thanks to enormous funding for educational programs, the whole world ?knows? that HIV causes AIDS. But is what we know compatible with the facts? This book challenges the conventional wisdom on this issue. Collating and analyzing, for the first time, the results of more than two decades of HIV testing, it reveals that the common assumptions about HIV and AIDS are incompatible with the published data. Among the many topics explored are the failings of HIV testing, statistical evidence that HIV is neither sexually transmitted nor increasingly prevalent, and problems caused by the differing diagnostic criteria for AIDS around the world. But how could everyone have been so wrong for so long? This vital question, unaddressed in previous works questioning the HIV-AIDS connection, is central to this book. The author considers comparable missteps of modern science, and discusses how funding influences discovery in today's scientific circles.

Biosimilars have the potential to change the way we think about, identify, and manage health problems. They are already impacting both clinical research and patient care, and this impact will only grow as our understanding and technologies improve. Written by a team of experienced specialists in clinical development, this book discusses various potential drug development strategies, the design and analysis of pharmacokinetics (PK) studies, and the design and analysis of efficacy studies.

This book demonstrates the importance and potential role of Traditional Ecological Knowledge in foreseeing and curbing future global pandemics. The reduction of species diversity has increased the risk of global pandemics and it is therefore not only imperative to articulate and disseminate knowledge on the linkages between human activities and the transmission of viruses to humans, but also to create policy pathways for operationalizing that knowledge to help solve future problems. Although this book has been prompted by the COVID-19 pandemic, it lays a policy foundation for the effective management or possible prevention of similar pandemics in the future. One effective way of establishing this linkage with a view to promoting planet health is by understanding the traditional ecological knowledge of indigenous peoples with a view to demonstrating the significant impact it has on keeping nature intact. This book argues for the deployment of traditional ecological knowledge for land use management in the preservation of biodiversity as a means for effectively managing the transmission of viruses from animals to humans and ensuring planetary health. The book is not projecting traditional ecological knowledge as a panacea to pandemics but rather accentuating its critical role in the effective mitigation of future pandemics. This book will be of great interest to students and scholars of traditional ecological knowledge, indigenous studies, animal ecology, environmental ethics and environmental studies more broadly.

The field of molecular evolution has experienced explosive growth in recent years due to the rapid accumulation of genetic sequence data, continuous improvements to computer hardware and software, and the development of sophisticated analytical methods. The increasing availability of large genomic data sets requires powerful statistical methods to analyse and interpret them, generating both computational and conceptual challenges for the field.
Computational Molecular Evolution provides an up-to-date and comprehensive coverage of modern statistical and computational methods used in molecular evolutionary analysis, such as maximum likelihood and Bayesian statistics. Yang describes the models, methods and algorithms that are most useful for analysing the ever-increasing supply of molecular sequence data, with a view to furthering our understanding of the evolution of genes and genomes. The book emphasizes essential concepts rather than mathematical proofs. It includes detailed derivations and implementation details, as well as numerous illustrations, worked examples, and exercises. It will be of relevance and use to students and professional researchers (both empiricists and theoreticians) in the fields of molecular phylogenetics, evolutionary biology, population genetics, mathematics, statistics and computer science. Biologists who have used phylogenetic software programs to analyze their own data will find the book particularly rewarding, although it should appeal to anyone seeking an authoritative overview of this exciting area of computational biology.

Advance Praise for Forensic Cardiovascular Medicine "Using his vast experience in the medical-legal theater, Dr. RuDusky addresses a very complicated field in a simple and concise manner. Stressing the importance of honesty, integrity, and accuracy of data, he provides an insightful and interesting approach to the problems facing today's medical community." - John H. Ellis, IV, M.D., F.A.C.C., Chairman, Department of Cardiology, Wyoming Valley Healthcare System and Wilkes-Barre General Hospital "... a must-read for any new and training physician ... I now understand why Dr. RuDusky is considered to be a medical expert in his own right." - Damien M. Marycz, M.D., Internal Medicine Resident, Cleveland Clinic "Dr. RuDusky has always been fair, impartial, logical, and concise in his analysis ... This book adds significantly to a better understanding of the complex medicine behind his opinions." - Robert K. Randall, Prominent Midwest malpractice attorney "...this book is significant ... a major contribution for use by physicians, physicians' students, lawyers and judges connected with the field of forensic medicine in many different contexts." -Michael D. Schottland, Prominent East Coast malpractice attorney An Essential Text from a World-Renowned Expert in Cardiology A unique resource for medical examiners and forensic specialists, Forensic Cardiovascular Medicine draws upon Dr. Basil RuDusky's vast experience to provide coverage of the clinical aspects of cardiac disease in a forensic context. The book emphasizes some of the most frequently encountered cardiovascular medical problems facing the medical examiner or forensic medical specialist, while also placing special emphasis on those conditions and disease states that are apt to be overlooked, misdiagnosed, or tardily considered. Covers death certificates, autopsies, and the role of the medical examiner Presents 25 case studies from the author's experience to illustrate concepts, including cardiac trauma, vascular abnormalities, specific cardiopathic disorders, and toxic, physical, technical, epidemiological, and social influences Contains the first complete classification of myocardial contusion and blunt cardiac trauma Includes a special section on the cardiovascular effects of breast cancer therapy Dr. RuDusky has served as a consultant for the federal government, corporations, insurance companies, and independent medical service agencies. He sits on the manuscript review board for several medical journals and is associate editor of Angiology, the official international journal of the American Society of Angiology and the International Academy of Clinical and Applied Thrombosis/Hemostasis. In this volume, he brings his considerable expertise to this survey of the topic.

In a global clinical development strategy, multiregional clinical trials (MRCTs) are vital in the development of innovative medicines. Multiregional Clinical Trials for Simultaneous Global New Drug Development presents a comprehensive overview on the current status of conducting MRCTs in clinical development. International experts from academia, industry, and health organizations address various aspects of the important problems in global clinical development and MRCTs. The book first provides a high-level introduction to the context, motivation, opportunities, and challenges in simultaneous global clinical development using MRCTs. It then focuses on the design, monitoring, and analysis/interpretation of MRCTs. The book concludes with an examination of the latest research topics from MRCT perspectives, such as special considerations by local health authorities, health economic evaluations, benefit-risk assessment, and medical devices. Explaining how to design, conduct, and interpret MRCTs, this book will help biostatisticians working in the late-stage clinical development of medical products. It will also be useful for statisticians and clinicians in the biopharmaceutical industry, regulatory agencies, and medical research institutes.

In Sleep medicine, as in most disciplines, understanding of epidemiology plays a crucial role in clinical treatment of sleep disorders. This issue discusses several recent, large epidemiologic studies with a specific focus on the clinical implications of the findings. Studies discussed include the Penn State Child Cohort, the Tucson Children's Assessment of Sleep Apnea Study, the Wisconsin Sleep Cohort Study, the Sleep Heart Health Study, the Bay Area Sleep Cohort, an ongoing study in Iceland, and the CARDIA Sleep Study.

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.

Spatial epidemiology is the description and analysis of the geographical distribution of disease. It is more important now than ever, with modern threats such as bio-terrorism making such analysis even more complex. This second edition of "Statistical Methods in Spatial Epidemiology" is updated and expanded to offer a complete coverage of the analysis and application of spatial statistical methods. The book is divided into two main sections: Part 1 introduces basic definitions and terminology, along with map construction and some basic models. This is expanded upon in Part II by applying this knowledge to the fundamental problems within spatial epidemiology, such as "disease mapping," "ecological analysis," "disease clustering," "bio-terrorism," "space-time analysis," "surveillance" and "infectious disease modelling," Provides a comprehensive overview of the main statistical methods used in spatial epidemiology. Updated to include a new emphasis on bio-terrorism and disease surveillance. Emphasizes the importance of space-time modelling and outlines the practical application of the method. Discusses the wide range of software available for analyzing spatial data, including WinBUGS, SaTScan and R, and features an accompanying website hosting related software. Contains numerous data sets, each representing a different approach to the analysis, and provides an insight into various modelling techniques.

This text is primarily aimed at medical statisticians, researchers and practitioners from public health and epidemiology. It is also suitable for postgraduate students of statistics and epidemiology, as well professionals working in government agencies.

This work explains the purpose of statistical methods in medical studies and analyzes the statistical techniques used by clinical investigators, with special emphasis on studies published in "The New England Journal of Medicine". It clarifies fundamental concepts of statistical design and analysis, and facilitates the understanding of research results.

Analysis of Correlated Data with SAS and R: 4th edition presents an applied treatment of recently developed statistical models and methods for the analysis of hierarchical binary, count and continuous response data. It explains how to use procedures in SAS and packages in R for exploring data, fitting appropriate models, presenting programming codes and results. The book is designed for senior undergraduate and graduate students in the health sciences, epidemiology, statistics, and biostatistics as well as clinical researchers, and consulting statisticians who can apply the methods with their own data analyses. In each chapter a brief description of the foundations of statistical theory needed to understand the methods is given, thereafter the author illustrates the applicability of the techniques by providing sufficient number of examples. The last three chapters of the 4th edition contain introductory material on propensity score analysis, meta-analysis and the treatment of missing data using SAS and R. These topics were not covered in previous editions. The main reason is that there is an increasing demand by clinical researchers to have these topics covered at a reasonably understandable level of complexity. Mohamed Shoukri is principal scientist and professor of biostatistics at The National Biotechnology Center, King Faisal Specialist Hospital and Research Center and Al-Faisal University, Saudi Arabia. Professor Shoukri's research includes analytic epidemiology, analysis of hierarchical data, and clinical biostatistics. He is an associate editor of the 3Biotech journal, a Fellow of the Royal Statistical Society and an elected member of the International Statistical Institute.

Randomization, Masking, and Allocation Concealment is indispensable for any trial researcher who wants to use state of the art randomization methods, and also wants to be able to describe these methods correctly. Far too often the subtle nuances that distinguish proper randomization from flawed randomization are completely ignored in trial reports that state only that randomization was used, with no additional information. Experience has shown that in many cases, the type of randomization that was used was flawed. It is only a matter of time before medical journals and regulatory agencies come to realize that we can no longer rely on (or publish) flawed trials, and that flawed randomization in and of itself disqualifies a trial from being robust or high quality, even if that trial is of high quality otherwise. This book will help to clarify the role randomization plays in ensuring internal validity, and in drawing valid inferences from the data. The various chapters cover a variety of randomization methods, and are not limited to the most common (and most flawed) ones. Readers will come away with a profound understanding of what constitutes a valid randomization procedure, so that they can distinguish the valid from the flawed among not only existing methods but also methods yet to be developed.

Guides You on the Development and Implementation of B-R Evaluations Benefit-Risk Assessment Methods in Medical Product Development: Bridging Qualitative and Quantitative Assessments provides general guidance and case studies to aid practitioners in selecting specific benefit-risk (B-R) frameworks and quantitative methods. Leading experts from industry, regulatory agencies, and academia present practical examples, lessons learned, and best practices that illustrate how to conduct structured B-R assessment in clinical development and regulatory submission. The first section of the book discusses the role of B-R assessments in medicine development and regulation, the need for both a common B-R framework and patient input into B-R decisions, and future directions. The second section focuses on legislative and regulatory policy initiatives as well as decisions made at the U.S. FDA's Center for Devices and Radiological Health. The third section examines key elements of B-R evaluations in a product's life cycle, such as uncertainty evaluation and quantification, quantifying patient B-R trade-off preferences, ways to identify subgroups with the best B-R profiles, and data sources used to assist B-R assessment. The fourth section equips practitioners with tools to conduct B-R evaluations, including assessment methodologies, a quantitative joint modeling and joint evaluation framework, and several visualization tools. The final section presents a rich collection of case studies. With top specialists sharing their in-depth knowledge, thought-provoking considerations, and practical advice, this book offers comprehensive coverage of B-R evaluation methods, tools, and case studies. It gives practitioners a much-needed toolkit to develop and conduct their own B-R evaluations.

Design and Analysis of Clinical Trials for Predictive Medicine provides statistical guidance on conducting clinical trials for predictive medicine. It covers statistical topics relevant to the main clinical research phases for developing molecular diagnostics and therapeutics-from identifying molecular biomarkers using DNA microarrays to confirming their clinical utility in randomized clinical trials. The foundation of modern clinical trials was laid many years before modern developments in biotechnology and genomics. Drug development in many diseases is now shifting to molecularly targeted treatment. Confronted with such a major break in the evolution toward personalized or predictive medicine, the methodologies for design and analysis of clinical trials is now evolving. This book is one of the first attempts to contribute to this evolution by laying a foundation for the use of appropriate statistical designs and methods in future clinical trials for predictive medicine. It is a useful resource for clinical biostatisticians, researchers focusing on predictive medicine, clinical investigators, translational scientists, and graduate biostatistics students.

Provides a concise and intuitive overview of the role of statistics in surgical practice. Uses no mathematics but includes narrative descriptions of statistical concepts. Includes topics of specific interest to surgeons. Provides real surgical examples to illustrate applications.

Epidemiological studies show that cancer incidence is far more dependent on the conditions of life than previously supposed. Classically, cancers occurred with heavy exposure to a specific occupational hazard, or were associated with habits. In some instances, research shows, the incidence of cancer falls when the method of work or the associated habit is changed. In short, variation in incidence is now known to be the rule rather than the exception in cancer. No cancer that occurs with even moderate frequency, occurs everywhere and always to the same extent. Sometimes it is even epidemic, similar in scale to an epidemic of infectious disease, but modified by the fact that the induction period may be as much as thirty years.

Prevention of cancer is now coming to be regarded as a practicable alternative to its cure. We remain almost totally ignorant of how cancer is produced at the cellular level and, until we know this, our methods of prevention are liable to be cumbersome and inefficient. Ethical considerations and the time scale of the disease make it impossible to obtain experimental evidence in man and what action to take has been determined from observing nature's experiments and by analogy from experiments in animals.

The evidence from epidemiological studies is of particular interest. Such studies suggest relationships that would never be thought of in the ordinary course of laboratory work and results that are directly relevant to the problems of human disease. The large numbers at risk and the intensity of the medical care to which people with cancer are subjected, make it possible to recognize relatively small improvements. Such practical decisions, based on information thus obtained, have largely eliminated the risk of cancer due to occupational hazards in several industries.

"Richard Doll" (1912-2005) was a British psychologist and one of the most prominent epidemiologists of the twentieth century. Throughout out his research he was able to link smoking with health problems and was the first individual to link smoking with lung cancer and increased risk of heart disease. He also studies the relationship between asbestos and lung cancer and radiation and leukemia. He is the author of many books, including "The Causes of Cancer: Quantitative Estimates of Avoidable Risks of Cancer in the United States Today" and "Tobacco and Health."

Containing method descriptions and step-by-step procedures, the Spatial Epidemiological Approaches in Disease Mapping and Analysis equips readers with skills to prepare health-related data in the proper format, process these data using relevant functions and software, and display the results as mapped or statistical summaries. Describing the wide range of available methods and key GIS concepts for spatial epidemiology, this book illustrates the utilities of the software using real-world data. Additional topics include geographic data models, address matching, geostatistical analysis, universal kriging, point pattern analysis, kernel density, spatio-temporal display, and disease surveillance.

Emphasis on concepts rather than formulas Includes interesting examples with real data Uses Stata and R