Books in the Statistics for Biology and Health series

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.

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 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 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 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.

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 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.