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Clinical Prediction Models

A Practical Approach to Development, Validation, and Updating

  • Ewout W. Steyerberg
Book

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

Table of contents

  1. Front Matter
    Pages i-xxxiii
  2. Ewout W. Steyerberg
    Pages 1-11
  3. Prediction Models in Medicine

    1. Front Matter
      Pages 13-13
    2. Ewout W. Steyerberg
      Pages 15-36
    3. Ewout W. Steyerberg
      Pages 37-58
    4. Ewout W. Steyerberg
      Pages 59-93
    5. Ewout W. Steyerberg
      Pages 95-112
    6. Ewout W. Steyerberg
      Pages 113-124
  4. Developing Valid Prediction Models

    1. Front Matter
      Pages 125-126
    2. Ewout W. Steyerberg
      Pages 127-155
    3. Ewout W. Steyerberg
      Pages 157-174
    4. Ewout W. Steyerberg
      Pages 175-190
    5. Ewout W. Steyerberg
      Pages 191-206
    6. Ewout W. Steyerberg
      Pages 207-225
    7. Ewout W. Steyerberg
      Pages 247-260
    8. Ewout W. Steyerberg
      Pages 261-275
    9. Ewout W. Steyerberg
      Pages 277-308
    10. Ewout W. Steyerberg
      Pages 309-328
    11. Ewout W. Steyerberg
      Pages 329-344
    12. Ewout W. Steyerberg
      Pages 345-363
  5. Generalizability of Prediction Models

    1. Front Matter
      Pages 365-365
    2. Ewout W. Steyerberg
      Pages 367-397
    3. Ewout W. Steyerberg
      Pages 399-429
    4. Ewout W. Steyerberg
      Pages 431-446
  6. Applications

    1. Front Matter
      Pages 449-449
    2. Ewout W. Steyerberg
      Pages 449-467
    3. Ewout W. Steyerberg
      Pages 495-518
  7. Back Matter
    Pages 519-558

About this book

Introduction

The second edition of this volume 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  a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice.

There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making.  In this Big Data era,  there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment.  Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps 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 formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. 

The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis.  While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. 


Updates to this new and expanded edition include:

• A discussion of Big Data and its implications for the design of prediction models

• Machine learning issues

• More simulations with missing ‘y’ values

• Extended discussion on between-cohort heterogeneity

• Description of ShinyApp

• Updated LASSO illustration

• New case studies 


Keywords

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

Authors and affiliations

  • Ewout W. Steyerberg
    • 1
  1. 1.Department of Biomedical Data SciencesLeiden University Medical CenterLeidenThe Netherlands

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-16399-0
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-16398-3
  • Online ISBN 978-3-030-16399-0
  • Series Print ISSN 1431-8776
  • Series Online ISSN 2197-5671
  • Buy this book on publisher's site
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