Title: Clinical prediction models
Subtitle: a practical approach to development, validation, and updating
Description: This book aims to provide 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 these innovations are insufficiently applied in medical research. Old-fashioned, data hungry methods are often used in data sets of limited size, validation of predictions is not done or only in a simplistic way, and updating of already available models is not considered. A sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. The text is primarily intended for epidemiologists and applied biostatisticians. It can be used as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. It is beneficial if readers are familiar with common statistical models in medicine: linea.
Subjects: Statistical methods, Statistiques, Recherche, Clinical trials, Statistiques médicales, Regression Analysis, Statistiques et données numériques, Research, Prognosis, Analyse de régression, Médecine, Médecine fondée sur la preuve, Evidence-based medicine, Études cliniques, Medical statistics, Medicine, Statistical Models, Medicine, research, Diagnosis, Methodology, Biomedical Research, Methods, Clinical Trials as Topic, Statistics, Statistics for Life Sciences, Medicine, Health Sciences