Dynamic prediction in clinical survival analysis
Read Online
Share

Dynamic prediction in clinical survival analysis by J. C. van Houwelingen

  • 603 Want to read
  • ·
  • 52 Currently reading

Published by CRC Press in Boca Raton .
Written in English

Subjects:

  • Survival Analysis,
  • Proportional Hazards Models

Book details:

Edition Notes

Includes bibliographical references and index.

StatementHans van Houwelingen, Hein Putter
SeriesMonographs on statistics and applied probability -- 123, Monographs on statistics and applied probability -- 123.
ContributionsPutter, Hein
The Physical Object
Paginationp. ;
ID Numbers
Open LibraryOL25054607M
ISBN 109781439835333
LC Control Number2011039592

Download Dynamic prediction in clinical survival analysis

PDF EPUB FB2 MOBI RTF

  Dynamic Prediction in Clinical Survival Analysis (Chapman & Hall/CRC Monographs on Statistics & Applied Probability Book ) 1, van Houwelingen, Hans, Putter, Hein - Dynamic Prediction in Clinical Survival Analysis (Chapman & Hall/CRC Monographs on Statistics & Applied Probability Book ) 1st Edition, Kindle Edition5/5(1). 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.   DOI link for Dynamic Prediction in Clinical Survival Analysis. Dynamic Prediction in Clinical Survival Analysis book. Dynamic Prediction in Clinical Survival Analysis book. By Hans van Houwelingen, Hein Putter. Edition 1st Edition. First Published eBook Published 9 November Pub. location Boca by: Dynamic Prediction in Clinical Survival Analysis | Hans, van | download | B–OK. Download books for free. Find books.

Abstract: "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. Dynamic Prediction in Clinical Survival Analysis Hans C. van Houwelingen Hein Putter CRC Press Taylor Francis Croup Boca Raton Londo n York CRC Press is an imprint of the Taylor Francis an business A CHAPMAN & HALL BOOK. Dynamic Prediction in Clinical Survival Analysis Using Temporal Convolutional Networks To the best of our knowledge, this is the first investigation of temporal convolutions in the context of dynamic prediction for personalized risk prognosis. Using real-world data from the Alzheimer's disease neuroimaging initiative, we demonstrate state.   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 s: 1.

Obtaining dynamic predictions by landmarking Additional remarks Dynamic prognostic models for survival data using time-dependent information Dynamic predictions using biomarkers Prediction in a dynamic setting Landmark prediction model Application Additional remarks Dynamic prediction in multi-state models Multi-state models in clinical. DOI link for Dynamic Prediction in Clinical Survival Analysis. Dynamic Prediction in Clinical Survival Analysis book. By Hans van Houwelingen, Hein Putter. Edition 1st Edition. First Published eBook Published 9 November Pub. location Boca Raton. Back to book Author: Hans van Houwelingen, Hein Putter. Get this from a library! Dynamic prediction in clinical survival analysis. [J C van Houwelingen; Hein Putter] -- "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 .   This study updates predictions of overall survival at different times during follow-up by using the concept of dynamic prediction. Patients and methods Information from patients with high-grade extremity soft tissue sarcoma, who underwent surgery at 14 specialized sarcoma centers, was used to develop a dynamic prediction model.