During the last twenty years statistical methodology has become of central importance in research studies in medicine and also in day-to-day clinical practice. The medical literature is now liberally punctuated not only with relatively routine statistical terms such as p-value, t-test, confidence interval, and correlation, but also with more esoteric items such as hazard function, multilevel model, generalized estimating equations and crossover design. Consequently researchers in medicine and clinicians who are not primarily statisticians need to have a source that provides readable accounts...

Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.

New to the Third Edition

• Three new chapters on quantile regression, missing values, and Bayesian inference
• Extra material in the logistic regression chapter that describes a regression model for ordered...

Much of the data collected in medicine and the social sciences is categorical, for example, sex, marital status, blood group, whether a smoker or not and so on, rather than interval-scaled. Frequently the researcher collecting such data is interested in the relationships or associations between pairs, or between a set of such categorical variables; often the data is displayed in the form of a contingency table for example, smoker versus non-smoker against death from lung cancer or death from some other cause. This text gives a comprehensive account of the analysis of such tables, written at a...