Multivariable Analysis
Now in its fourth edition, this best-selling, highly praised text has been fully revised and updated with expanded sections on propensity analysis, sensitivity analysis, and emulation trials. As before, it focuses on easy-to follow explanations of complicated multivariable techniques including logistic regression, proportional hazards analysis, and Poisson regression. The perfect introduction for medical researchers, epidemiologists, public health practitioners, and health service researchers, this book describes how to preform and interpret multivariable analysis, using plain language rather than mathematical formulae. It takes advantage of the availability of user-friendly software that allow novices to conduct complex analysis without programming experience; ensuring that these analyses are set up and interpreted correctly. Numerous tables, graphs, and tips help to demystify the process of performing multivariable analysis. The text is illustrated with many up-to-date examples from the published literature that enable readers to model their analyses after well conducted research, increasing chances of top-tier publication.
- Explains complex biostatistical concepts without the need for complex math, allowing researchers to conduct analyses without studying advanced calculus
- Provides examples from the published literature, allowing analyses to model successfully published papers
- Illustrated throughout with up-to-date examples from the medical literature, tables and graphs to help simplify the process of performing multivariable analysis
Product details
October 2025Paperback
9781009558471
306 pages
246 × 189 mm
Not yet published - available from October 2025
Table of Contents
- 1. Introduction
- 2. Common uses of multivariable models
- 3. Outcome variables in multivariable analysis
- 4. Independent variables in multivariable analysis
- 5. Relationship of independent variables to one another
- 6. Setting up a multivariable analysis
- 7. Performing the analysis
- 8. Interpreting the results
- 9. Delving deeper: checking the underlying assumptions of the analysis
- 10. Propensity scores
- 11. Correlated observations
- 12. Sensitivity Analysis
- 13. Validation of models
- 14. Special topics
- 15. Publishing your study
- 16. Summary: Steps for constructing a multivariable model
- Index.