Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating
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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Clinical Prediction Models: A Practical Approach to Development, Validation ...
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30-day mortality adjusted approach assessment baseline bias binary outcomes blood pressure bootstrap bootstrap sample calculated calibration slope calibration-in-the-large candidate predictors cardiovascular case-mix centres Chap characteristics Clinical Prediction Models coding consider continuous predictors correlation covariate curve cutoff data set diagnostic differences disease distribution effects of predictors estimated example external validation factor function hazard ratios hence hospital imputation model included intercept Killip Killip class likelihood linear predictor linear regression logistic regression logistic regression model methods missing values missingness model development model performance multivariable non-linear odds ratios optimism overall overfitting p-value penalized Predicted Probability prediction model predictor values procedure prognostic model random re-calibration re-estimated regression analysis regression coefficients relatively resection risk score shrinkage specific spline Springer Science+Business Media standard statistic stepwise selection Steyerberg EW subsamples survival Table testicular cancer TIMI-II tion transformations traumatic brain injury treatment tumor univariate updating validation sample validation setting variables zero