Bücher der Reihe Statistics for Biology and Health

This fresh edition, substantially revised and augmented, provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics. The examples used, analyzed using Stata, can be applied to other areas.

This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis).

This book examines statistical techniques that are critically important to Chemistry, Manufacturing, and Control (CMC) activities.

This book is a primer for readers interested in learning more about this fascinating subject and the many statistical challenges inherent in functional neuroimaging data. It presents the basics of technique and surveys popular statistical approaches.

The book introduces the reader to methodological aspects of epidemiology that are specific for infectious diseases and provides insight into the epidemiology of some classes of infectious diseases characterized by their main modes of transmission.

Examining the etiology of cancer in large human populations using mathematical models developed from an inter-disciplinary perspective, this book investigates how tumor initiation relates to general processes of senescence and to other major chronic diseases

This book details the statistical concepts used in gene mapping. It presents elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language.

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.

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.

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.

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.

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.

Prediction models are important in various fields, including medicine, physics, meteorology, and finance. Prediction models will become more relevant in the medical field with the increase in knowledge on potential predictors of outcome, e.g.

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.

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.

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 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.

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.

A full four-color book that illustrates publicly available data and includes worked case studies.

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.

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.

Applied statisticians in many fields must frequently analyze time to event data.

This book surveys statistical aspects of designing, analyzing and interpreting results of genome-wide association scans for genetic causes of disease, using unrelated subjects. Covers bioinformatics and data handling methods needed to ready data for analysis.

This book offers statistical models and methods used to understand human genetics, focusing on modern approaches to association analysis. Numerous examples illustrate key points, and the text includes exercises for students with a broad range of skill levels.

Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine.

This very popular textbook, now in its third edition, offers an accessible description of fundamental and more advanced concepts and methods of logistic regression. This edition includes three new chapters and an expanded section about modeling guidelines.

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 text bridges the gap between standard models, and those where the dynamic structure of the data manifests itself fully. The common thread is stochastic processes. The authors show how martingales and stochastic integrals fit with censored data.

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.