Regression Methods in Biostatistics : Linear Logistic Survival and Repeated Measures Models Introduction Exploratory and Descriptive Methods Basic Statistical Methods Linear Regression Logistic Regression Survival Analysis Repeated Measures Analysis Generalized Linear Models Strengthening Casual Inference Predictor Selection Complex Surveys Summary. This new edition provides a unified in depth readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes logistic models for binary outcomes the Cox model for right censored survival times repeated measures models for longitudinal and hierarchical outcomes and generalized linear models for counts and other outcomes. Treating these topics together takes advantage of all they have in common. The authors point out the many shared elements in the methods they present for selecting estimating checking and interpreting each of these models. They also show that these regression methods deal with confounding mediation and interaction of causal effects in essentially the same way. The examples analyzed using Stata are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods this one book may be all they need to conduct and interpret multipredictor regression analyses. In the second edition the authors have substantially expanded the core chapters including new coverage of exact ordinal and multinomial logistic models discrete time and competing risks survival models within and between effects in longitudinal models zero inflated Poisson and negative binomial models cross validation for prediction model selection directed acyclic graphs and sample size power and minimum detectable effect calculations; Stata code is also updated. In addition there are new chapters on methods for strengthening causal inference including propensity scores marginal structural models and instrumental variables and on methods for handling missing data using maximum likelihood multiple imputation inverse weighting and pattern mixture models. From the reviews of the first edition: This book provides a unified introduction to the regression methods listed in the title...The methods are well illustrated by data drawn from medical studies...A real strength of this book is the careful discussion of issues common to all of the multipredictor methods covered. Journal of Biopharmaceutical Statistics 2005 This book is not just for biostatisticians. It is in fact a very good and relatively nonmathematical overview of multipredictor regression models. Although the examples are biologically oriented they are generally easy to understand and follow...I heartily recommend the book Technometrics February 2006 Overall the text provides an overview of regression methods that is particularly strong in its breadth of coverage and emphasis on insight in place of mathematical detail. As intended this well unified approach should appeal to students who learn conceptually and verbally. Journal of the American Statistical Association March 2006.