Isbn: 9781032925158 - dynamic prediction in clinical survival analysis (chapman & hall/crc monographs on statistics and applied probability) (3 Ergebnisse)

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    • Sprache: Englisch

      Verlag: CRC Press, 2024

      1032925159 / 9781032925158

      Serie: Buch 37 von 110 - ISSN

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    • Sprache: Englisch

      Verlag: CRC Press, 2024

      1032925159 / 9781032925158

      Serie: Buch 37 von 110 - ISSN

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      Zustand: New. Hans van Houwelingen received his Ph.D. in Mathematical Statistics from the University of Utrecht in 1973. He stayed at the Mathematics Department in Utrecht until 1986. In that time his theoretical research interest was empirical Bayes .

    • Sprache: Englisch

      Verlag: CRC Press Okt 2024, 2024

      1032925159 / 9781032925158

      Serie: Buch 37 von 110 - ISSN

      • Softcover

      Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH

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      Taschenbuch. Zustand: Neu. Neuware - 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.