Bøger i Statistics for Biology and Health serien

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.

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.

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.

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 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 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.

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 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.

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.

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.

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

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.

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.

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 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.

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