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Multivariable regression models are widely used in all areas of science in which empirical data are analysed. Using the multivariable fractional polynomials (MFP) approach this book focuses on the selection of important variables and the determination of functional form for continuous predictors. Despite being relatively simple, the selected models often extract most of the important information from the data. The authors have chosen to concentrate on examples drawn from medical statistics, although the MFP method has applications in many other subject-matter areas as well.

 

Multivariable Model-Building:

* Focuses on normal-error models for continuous outcomes, logistic regression for binary outcomes and Cox regression for censored time-to-event data.

* Concentrates on fractional polynomial models and illustrates new approaches to model critisism and stability.

* Provides comparisons with and discussion of other techniques such as spline models.

* Features new strategies on modelling interactions with continuous covariates which are important in the context of randomized trials and observational studies

* Does not consider high-dimensional data, such as gene expression data.

* Is illustrated throughout with working examples from more than 20 substantial real datasets, most data sets and programs in Stata are available on a website enabling the reader to apply techniques directly

* Is written in an accessible and informal style making it suitable for researchers from a range of disciplines with minimal mathematical background

 

This book provides a readable text giving the rationale of, and practical advice on, a unified approach to multivariable modelling. It aims to make multivariable model building simpler, transparent and more effective. This book is aimed at graduate students studying regression modelling and professionals in statistics as well as researchers from medical, physical, social and many other sciences where regression models play a central role.