Clinical Prediction Models

Despite advances in statistical approaches towards clinical outcome prediction, these innovations are insufficiently utilized in medical research. This book provides information on how modern statistical concepts and regression methods can be applied.

Features, in this new edition, a discussion of Big Data and its implications of the design of prediction models
Includes, in this new edition, new case studies, more simulations with missing "y" values, description of ShinyApp, and more
Presents a practical checklist to be consulted for the development of a valid prediction model, ideal for clinical epidemiologists and biostatisticians alike

Autorentext
Ewout Steyerberg worked for 25 years at Erasmus Medical Center in Rotterdam before moving to Leiden where he is now Professor of Clinical Biostatistics and Medical Decision Making and chair of the Department of Biomedical Data Sciences at Leiden University Medical Center. His research has covered a broad range of methodological and medical topics, which is reflected in hundreds of peer-reviewed methodological and applied publications. His methodological expertise is in the design and analysis of randomized controlled trials, cost-effectiveness analysis, and decision analysis. His methodological research focuses on the development, validation and updating of prediction models, as reflected in a textbook (Springer, 2009). His medical fields of application include oncology, cardiovascular disease, internal medicine, pediatrics, infectious diseases, neurology, surgery and traumatic brain injury.

Inhalt
Introduction.- Applications of prediction models.Study design for prediction modeling.- Statistical Models for Prediction.- Overfitting and optimism in prediction models.- Choosing between alternative statistical models.- Missing values.- Case study on dealing with missing values.- Coding of Categorical and Continuous Predictors.- Restrictions on candidate predictors.- Selection of main effects.- Assumptions in regression models: Additivity and linearity.- Modern estimation methods.- Estimation with external information.- Evaluation of performance.- Evaluation of Clinical Usefulness.- Validation of Prediction Models.- Presentation formats.- Patterns of external validity.- Updating for a new setting.- Updating for multiple settings.- Case study on a prediction of 30-day mortality.- Case study on Survival Analysis: prediction of cardiovascular events.- Overall lessons and data sets.- References.