Dynamic prediction in clinical survival analysis /

"In the last twenty years, dynamic prediction models have been extensively used to monitor patient prognosis in survival analysis. Written by one of the pioneers in the area, this book synthesizes these developments in a unified framework. It covers a range of models, including prognostic and dynamic prediction of survival using genomic data and time-dependent information. The text includes numerous examples using real data that is taken from the authors collaborative research. R programs are provided for implementing the methods"--Provided by publisher.Includes bibliographical references (p. 217-231) and index.The special nature of survival data -- Cox regression model -- Measuring the predictive value of a Cox model -- Calibration and revision of Cox models -- Mechanisms explaining violation of the Cox model -- Non-proportional hazards models -- Dealing with non-proportional hazards -- Dynamic predictions using biomarkers -- Dynamic prediction in multi-state models -- Dynamic prediction in chronic disease -- Penalized Cox models -- Dynamic prediction based on genomic data."In the last twenty years, dynamic prediction models have been extensively used to monitor patient prognosis in survival analysis. Written by one of the pioneers in the area, this book synthesizes these developments in a unified framework. It covers a range of models, including prognostic and dynamic prediction of survival using genomic data and time-dependent information. The text includes numerous examples using real data that is taken from the authors collaborative research. R programs are provided for implementing the methods"--Provided by publisher