Multivariable analysis : a practical guide for clinicians / Mitchell H. Katz.

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Introduction -- Common uses of multivariable models -- Outcome variables in multivariable analysis -- Type of independent variables in multivariable analysis -- Assumptions of multiple linear regression, multiple logistic regression, and proportional hazards analysis -- Relationship of independent variables to one another -- Setting up a multivariable analysis -- Performing the analysis -- Interpreting the analysis -- Checking the assumptions of the analysis -- Propensity scores -- Correlated observations -- Validation of models -- Special topics -- Publishing your study -- Summary: steps for constructing a multivariable model.

This new edition has been fully revised to build on the enormous success of its popular predecessor. It now includes new features introduced by readers' requests including a new chapter on propensity score, more detail on clustered data and Poisson regression and a new section on analysis of variance. As before it describes how to perform and interpret multivariable analysis, using plain language rather than complex derivations and mathematical formulae. It is the perfect introduction for all clinical researchers. It focuses on the nuts and bolts of performing research and prepares the reader to perform and interpret multivariable models. Numerous tables, graphs and tips help to simplify and explain the process of performing multivariable analysis. The text is illustrated with many up-to-date examples from the medical literature on how to use multivariable analysis in clinical practice and in research.