Bücher der Reihe Statistics for Biology and Health

This book explains statistical methods for finding and estimating seasonal patterns. It describes methods for a range of outcomes (including continuous, count and binomial data) and demonstrates appropriate techniques for summarizing and modeling these data.

This highly readable book describes fundamental and advanced concepts and methods of logistic regression. The 3rd edition includes three new chapters, an updated computer appendix, and an expanded section on modeling guidelines that consider causal diagrams.

This book offers a modern perspective on statistical methods for detecting disease clustering. It offers analysis and illustration of methods for a variety of real data sets, and will provide an invaluable resource for a wide ranging of audience.

This book shows how to model heterogeneity in medical research with covariate adjusted finite mixture models. The areas of application include epidemiology, gene expression data, disease mapping, meta-analysis, neurophysiology and pharmacology.

Each chapter consists of basic statistical theory, simple examples of S-PLUS code, plus more complex examples of S-PLUS code, and exercises.

Building on their previous book on the subject, the authors provide an expanded introduction to using Regression to analyze ecological data. As with the earlier book, real data sets from postgraduate ecological studies or research projects are used throughout.

Written to equip students in the mathematical siences to understand and model the epidemiological and experimental data encountered in genetics research. This second edition expands the original edition by over 100 pages and includes new material. Sprinkled throughout the chapters are many new problems.

This book presents models and statistical methods for the analysis of recurrent event data. More general intensity-based models are also considered, as well as simpler models that focus on rate or mean functions.

This book focuses on the theory and applications of a very broad class of models which underlie modern survival analysis. However, this text differs from most recent works in that it is mostly concerned with methodological issues rather than the analysis itself.

All three of this work's authors are experts in adaptive methodology for clinical trials. Here, they offer an accessible, incremental approach to understanding Brownian motion as related to clinical trials that will develop insight into not only monitoring, but many other statistical issues germane to clinical trials.

This book provides a practical introduction to analyzing ecological data using real data sets. It features 17 case studies covering topics ranging from terrestrial ecology to marine biology and can be used as a template for a reader's own data analysis.

Here is a new book on methods and issues in clinical research. Integrate medical and statistical components of clinical research. Give space to the ethical implications of methodological issues in clinical research. The book ends with a brief description of the drug development process and the phases of clinical development.

Aimed at graduate students and researchers and including statistical models and analytical procedures, this book introduces the basic concepts and methods used in the statistical analysis and modeling of the DNA-based marker and phenotypic data that arise in agriculture and related fields.

Quantitative trait locus (QTL) mapping is used to discover the genetic and molecular architecture underlying complex quantitative traits. This illustrated book is a comprehensive guide to the practice of QTL mapping and the use of R/qtl.

As well as being a reference for the design, analysis, and interpretation of vaccine studies, the text covers all design and analysis stages, from vaccine development to post-licensure surveillance, presenting likelihood, frequentists, and Bayesian approaches.

This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and alternative models with the aim of describing time-varying effects of explanatory variables.

Focuses on applications of demographic models, extending to matrix models for stage-classified populations. This book introduces the life table to describe age-specific mortality, and develops theory for stable populations and the rate of population increase. It also introduces reproductive value and the stable equivalent population.

This book provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Effort has been made to relate biological to statistical parameters throughout, and extensive examples are included to illustrate the arguments.

Using a rigorous account of statistical forecasting efforts that led to the successful resolution of the John-Manville asbestos litigation, the models in this volume can be adapted to forecast industry-wide asbestos liability.

Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent. As the field is rather new, the concepts and the possible types of data are described in detail.

This book is for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyze multiple/correlated event data using marginal and random effects.

Treats such biological topics as sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes. This title covers statistical techniques that include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods.

This guide to the latest statistical techniques for estimating the parameters of infectious diseases arises from a groundbreaking collaboration between Hasselt and Antwerp universities in Belgium, and features valuable case studies and software advice.

This book serves as a reference text for regulatory, industry and academic statisticians and also a handy manual for entry level Statisticians. Additionally it aims to stimulate academic interest in the field of Nonclinical Statistics and promote this as an important discipline in its own right.

Epidemiologic Studies in Cancer Prevention and Screening is the first comprehensive overview of the evidence base for both cancer prevention and screening. The main aim of the book is to provide a realistic appraisal of the evidence for both cancer prevention and cancer screening.

This edited volume is a definitive text on adaptive clinical trial designs from creation and customization to utilization. As this book covers the full spectrum of topics involved in the adaptive designs arena, it will serve as a valuable reference for researchers working in industry, government and academia. The target audience is anyone involved in the planning and execution of clinical trials, in particular, statisticians, clinicians, pharmacometricians, clinical operation specialists, drug supply managers, and infrastructure providers. In spite of the increased efficiency of adaptive trials in saving costs and time, ultimately getting drugs to patients sooner, their adoption in clinical development is still relatively low. One of the chief reasons is the higher complexity of adaptive design trials as compared to traditional trials. Barriers to the use of clinical trials with adaptive features include the concerns about the integrity of study design and conduct, the risk of regulatory non-acceptance, the need for an advanced infrastructure for complex randomization and clinical supply scenarios, change management for process and behavior modifications, extensive resource requirements for the planning and design of adaptive trials and the potential to relegate key decision makings to outside entities. There have been limited publications that address these practical considerations and recommend best practices and solutions. This book fills this publication gap, providing guidance on practical considerations for adaptive trial design and implementation. The book comprises three parts: Part I focuses on practical considerations from a design perspective, whereas Part II delineates practical considerations related to the implementation of adaptive trials. Putting it all together, Part III presents four illustrative case studies ranging from description and discussion of specific adaptive trial design considerations to the logistic and regulatory issues facedin trial implementation.Bringing together the expertise of leading key opinion leaders from pharmaceutical industry, academia, and regulatory agencies, this book provides a balanced and comprehensive coverage of practical considerations for adaptive trial design and implementation.

This book serves as a reference text for regulatory, industry and academic statisticians and also a handy manual for entry level Statisticians. Additionally it aims to stimulate academic interest in the field of Nonclinical Statistics and promote this as an important discipline in its own right.

Both humanitarian and commercial considerations have spurred intensive search for methods to reduce the time and cost required to develop new therapies. The identification and use of surrogate endpoints, i.e., measures that can replace or supplement other endpoints in evaluations of experimental treatments or other interventions, is a general strategy that has stimulated both enthusiasm and skepticism. Surrogate endpoints are useful when they can be measured earlier, more conveniently, or more frequently than the "true" endpoints of primary interest. Regulatory agencies around the globe, particularly in the United States, Europe, and Japan, are introducing provisions and policies relating to the use of surrogate endpoints in registration studies. But how can one establish the adequacy of a surrogate? What kind of evidence is needed, and what statistical methods portray that evidence most appropriately? This book offers a balanced account on this controversial topic. The text presents major developments of the last couple of decades, together with a unified, meta-analytic framework within which surrogates can be evaluated from several angles. Methodological development is coupled with perspectives on various therapeutic areas. Academic views are juxtaposed with standpoints of scientists working in the biopharmaceutical industry as well as of colleagues from the regulatory authorities.Tomasz Burzykowski is Assistant Professor of Biostatistics at the Limburgs Universitair Centrum in Belgium. Dr. Burzykowski has published methodological work on the analysis of survey data, meta-analyses of clinical trials, and validation of surrogate endpoints. He is a co-author of numerous papers applying statistical methods to clinical data in different disease areas (cancer, cardiovascular diseases, dermatology, orthodontics).Geert Molenberghs is Professor of Biostatistics at the Limburgs Universitair Centrum in Belgium. Dr. Molenberghs published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and on the analysis of non-response in clinical and epidemiological studies. He serves as Joint Editor for Applied Statistics (2001-2004) and is President of the International Biometric Society (2004-2005). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society. Marc Buyse founded the International Drug Development Institute in 1991. He is Past President of the International Society for Clinical Biostatistics, Past President of the Quetelet Society, and Past Board Member of the Society for Clinical Trials. He is currently the Executive Director of IDDI (International Drug Development Institute) and Associate Professor of biostatistics at the Limburgs Universitair Centrum, Center for Statistics, Diepenbeek, Belgium. He has published extensively in the fields of biostatistics and oncology. His research interests include meta-analysis, surrogate endpoints, statistical detection of fraud, and the design and statistical analysis of clinical trials.From the reviews:"A strength of this book is its comprehensive and up-to-date presentation of issues pertinent to the evaluation of surrgoate endpoints...This book makes an important contribution to the clinical trials literature..." Journal of Biopharmaceutical Statistics, 2006"Many of the chapters deal with real-life data examples and studies involving surrogate outcomes, many written by authors who were directly involved in these studies...The editors have written nice background sections...until a more concise manuscript on this topic is written, this book will remain the most important resource for biostatisticians and researchers in this a

Epidemiologic Studies in Cancer Prevention and Screening is the first comprehensive overview of the evidence base for both cancer prevention and screening. The main aim of the book is to provide a realistic appraisal of the evidence for both cancer prevention and cancer screening.

Practical Considerations for Adaptive Trial Design and Implementation