Prediction models are important in various fields, including
medicine, physics, meteorology, and finance. Prediction models will
become more relevant in the medical field with the increase in
knowledge on potential predictors of outcome, e.g. from genetics.
Also, the number of applications will increase, e.g. with targeted
early detection of disease, and individualized approaches to
diagnostic testing and treatment. The current era of evidence-based
medicine asks for an individualized approach to medical
decision-making. Evidence-based medicine has a central place for
meta-analysis to summarize results from randomized controlled
trials; similarly prediction models may summarize the effects of
predictors to provide individu- ized predictions of a diagnostic or
prognostic outcome. Why Read This Book? My motivation for working
on this book stems primarily from the fact that the development and
applications of prediction models are often suboptimal in medical
publications. With this book I hope to contribute to better
understanding of relevant issues and give practical advice on
better modelling strategies than are nowadays widely used. Issues
include: (a) Better predictive modelling is sometimes easily
possible; e.g. a large data set with high quality data is
available, but all continuous predictors are dich- omized, which is
known to have several disadvantages.
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