TY - BOOK ID - 48089591 TI - Clinical Prediction Models : A Practical Approach to Development, Validation, and Updating PY - 2019 SN - 3030163989 3030163997 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Medical statistics. KW - Medicine KW - Evidence-based medicine KW - Research KW - Statistical methods. KW - Statistics. KW - Internal medicine. KW - Statistics for Life Sciences, Medicine, Health Sciences. KW - Internal Medicine. KW - Medicine, Internal KW - Statistical analysis KW - Statistical data KW - Statistical methods KW - Statistical science KW - Mathematics KW - Econometrics KW - Statistics . KW - Models, Statistical. KW - Regression Analysis. KW - Analysis, Regression KW - Regression Diagnostics KW - Statistical Regression KW - Analyses, Regression KW - Diagnostics, Regression KW - Regression Analyses KW - Regression, Statistical KW - Regressions, Statistical KW - Statistical Regressions KW - Statistics as Topic KW - Model, Statistical KW - Models, Binomial KW - Models, Polynomial KW - Statistical Model KW - Probabilistic Models KW - Statistical Models KW - Two-Parameter Models KW - Binomial Model KW - Binomial Models KW - Model, Binomial KW - Model, Polynomial KW - Model, Probabilistic KW - Model, Two-Parameter KW - Models, Probabilistic KW - Models, Two-Parameter KW - Polynomial Model KW - Polynomial Models KW - Probabilistic Model KW - Two Parameter Models KW - Two-Parameter Model KW - Health Workforce KW - Statistics KW - Health KW - Health statistics UR - https://www.unicat.be/uniCat?func=search&query=sysid:48089591 AB - 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 . ER -