Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating

✍ Scribed by Ewout W. Steyerberg (auth.)

• Publisher: Springer-Verlag New York
• Year: 2009
• Tongue: English
• Leaves: 508
• Series: Statistics for Biology and Health
• Edition: 1
• Category: Library

✦ Synopsis

This book provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but these innovations are insufficiently applied in medical research. Old-fashioned, data hungry methods are often used in data sets of limited size, validation of predictions is not done or done simplistically, and updating of previously developed models is not considered. A sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice.

Clinical prediction models presents a practical checklist with seven steps that need to be considered for development of a valid prediction model. These include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formats. The steps are illustrated with many small case-studies and R code, with data sets made available in the public domain. The book further focuses on generalizability of prediction models, including patterns of invalidity that may be encountered in new settings, approaches to updating of a model, and comparisons of centers after case-mix adjustment by a prediction model.

The text is primarily intended for clinical epidemiologists and biostatisticians. It can be used as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. It is beneficial if readers are familiar with common statistical models in medicine: linear regression, logistic regression, and Cox regression. The book is practical in nature. But it provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. In this era of evidence-based medicine, randomized clinical trials are the basis for assessment of treatment efficacy. Prediction models are key to individualizing diagnostic and treatment decision making.

Ewout Steyerberg (1967) is Professor of Medical Decision Making, in particular prognostic modeling, at Erasmus MC–University Medical Center Rotterdam, the Netherlands. His work on prediction models was stimulated by various research grants including a fellowship from the Royal Netherlands Academy of Arts and Sciences. He has published over 250 peer-reviewed articles in collaboration with many clinical researchers, both in methodological and medical journals.

✦ Table of Contents

Front Matter....Pages i-xxviii
Introduction....Pages 1-7
Applications of prediction models....Pages 11-31
Study design for prediction models....Pages 33-52
Statistical Models for Prediction....Pages 53-82
Overfitting and optimism in prediction models....Pages 83-100
Choosing between alternative statistical models....Pages 101-111
Dealing with missing values....Pages 115-137
Case study on dealing with missing values....Pages 139-157
Coding of Categorical and Continuous Predictors....Pages 159-173
Restrictions on candidate predictors....Pages 175-189
Selection of main effects....Pages 191-211
Assumptions in regression models:Additivity and linearity....Pages 213-230
Modern estimation methods....Pages 231-242
Estimation with external information....Pages 243-254
Evaluation of performance....Pages 255-280
Clinical Usefulness....Pages 281-297
Validation of Prediction Models....Pages 299-311
Presentation formats....Pages 313-331
Patterns of external validity....Pages 335-360
Updating for a new setting....Pages 361-389
Updating for multiple settings....Pages 391-408
Prediction of a binary outcome:30-day mortality after acute myocardial infarction....Pages 411-426
Case study on survival analysis:prediction of secondary cardiovascular events....Pages 427-446
Lessons from case studies....Pages 447-462
Back Matter....Pages 463-497

✦ Subjects

Statistics for Life Sciences, Medicine, Health Sciences