Introduction -- Applications of prediction models -- Study design for prediction modeling -- Statistical models for prediction -- Overfitting and optimism in prediction models -- Choosing between alternative 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.
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SUMMARY OR ABSTRACT
Text of Note
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 individualized predictions of a diagnostic or prognostic outcome.