Clinical Prediction Models

A Practical Approach to Development, Validation, and Updating

  • Ewout W. Steyerberg

Part of the Statistics for Biology and Health book series (SBH)

Table of contents

  1. Front Matter
    Pages i-xxviii
  2. E.W. Steyerberg
    Pages 1-7
  3. E.W. Steyerberg
    Pages 11-31
  4. E.W. Steyerberg
    Pages 33-52
  5. E.W. Steyerberg
    Pages 53-82
  6. E.W. Steyerberg
    Pages 83-100
  7. E.W. Steyerberg
    Pages 101-111
  8. E.W. Steyerberg
    Pages 115-137
  9. E.W. Steyerberg
    Pages 139-157
  10. E.W. Steyerberg
    Pages 159-173
  11. E.W. Steyerberg
    Pages 175-189
  12. E.W. Steyerberg
    Pages 191-211
  13. E.W. Steyerberg
    Pages 231-242
  14. E.W. Steyerberg
    Pages 243-254
  15. E.W. Steyerberg
    Pages 255-280
  16. E.W. Steyerberg
    Pages 281-297
  17. E.W. Steyerberg
    Pages 299-311
  18. E.W. Steyerberg
    Pages 313-331
  19. E.W. Steyerberg
    Pages 335-360
  20. E.W. Steyerberg
    Pages 361-389
  21. E.W. Steyerberg
    Pages 391-408
  22. E.W. Steyerberg
    Pages 447-462
  23. Back Matter
    Pages 463-497

About this book


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.


Anästhesie-Informations-Management-System Data-analysis Evidence-Based Medicine Prediction Radiologieinformationssystem Regression modeling Validation coding data analysis diagnosis linear regression

Authors and affiliations

  • Ewout W. Steyerberg

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