Free download and read ebooks online Dynamic Prediction In Clinical Survival Analysis by Hans van Houwelingen, Hein Putter full book PDF ePUB Audiobook. Download a free ebooks and kindle audiobook Dynamic Prediction In Clinical Survival Analysis with ISBN id 9781439835333 full page with book summary. Hans van Houwelingen book PDF ePUB of Dynamic Prediction In Clinical Survival Analysis published by CRC Press in 2011-11-09. Dynamic Prediction in Clinical Survival Analysis released with category Mathematics with total 250 pages.
📖 Dynamic Prediction In Clinical Survival Analysis by Hans van Houwelingen read book online
There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime after diagnosis or treatment. In contrast, Dynamic Prediction in Clinical Survival Analysis focuses on dynamic models for the remaining lifetime at later points in time, for instance using landmark models. Designed to be useful to applied statisticians and clinical epidemiologists, each chapter in the book has a practical focus on the issues of working with real life data. Chapters conclude with additional material either on the interpretation of the models, alternative models, or theoretical background. The book consists of four parts: Part I deals with prognostic models for survival data using (clinical) information available at baseline, based on the Cox model Part II is about prognostic models for survival data using (clinical) information available at baseline, when the proportional hazards assumption of the Cox model is violated Part III is dedicated to the use of time-dependent information in dynamic prediction Part IV explores dynamic prediction models for survival data using genomic data Dynamic Prediction in Clinical Survival Analysis summarizes cutting-edge research on the dynamic use of predictive models with traditional and new approaches. Aimed at applied statisticians who actively analyze clinical data in collaboration with clinicians, the analyses of the different data sets throughout the book demonstrate how predictive models can be obtained from proper data sets.
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