Models for Multi-State Survival Data - Andersen, Per Kragh (Biostatistics, University of Copenhagen); Ravn, Henrik (Novo Nordisk A/S)
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Multi-state models provide a statistical framework for studying longitudinal data on subjects when focus is on the occurrence of events that the subjects may experience over time. They find application particularly in biostatistics, medicine, and public health.

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Produktbeschreibung
Multi-state models provide a statistical framework for studying longitudinal data on subjects when focus is on the occurrence of events that the subjects may experience over time. They find application particularly in biostatistics, medicine, and public health.
Autorenporträt
Henrik Ravn is senior statistical director at Novo Nordisk A/S, Denmark. He graduated with an MSc in theoretical statistics in 1992 from University of Aarhus, Denmark and completed a PhD in biostatistics in 2002 from the University of Copenhagen, Denmark. He joined Novo Nordisk in late 2015 after more than 22 years of experience from biostatistical and epidemiological research, at Statens Serum Institut, Denmark and in Guinea-Bissau, West Africa. He has co-authored more than 160 papers, mainly within epidemiology and application of survival analysis and has taught several courses as external lecturer at Section of Biostatistics, University of Copenhagen. Per Kragh Andersen is professor of biostatistics at the Department of Public Health, University of Copenhagen, Denmark since 1998. He graduated in mathematical statistics from University of Copenhagen in 1978, got his PhD in 1982 and a DMSc degree in 1997. From 1993 to 2002 he worked half time as chief statistician at Danish Epidemiology Science. He is an author or co-author of more than 125 papers on statistical methodology and more than 250 papers in the medical literature. His research has concentrated on survival analysis and he is a co-author of the 1993 book 'Statistical Models Based on Counting Processes'. He has taught several courses both nationally and internationally both for students with a mathematical background and for students in medicine or public health.