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Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data

This paper considers the problem of obtaining a dynamic prediction for 5-year failure free survival after bone marrow transplantation in ALL patients using data from the EBMT, the European Group for Blood and Marrow Transplantation. The paper compares the new landmark methodology as developed by the...

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Detalles Bibliográficos
Autores principales: van Houwelingen, Hans C., Putter, Hein
Formato: Texto
Lenguaje:English
Publicado: Springer US 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2798037/
https://www.ncbi.nlm.nih.gov/pubmed/18836831
http://dx.doi.org/10.1007/s10985-008-9099-8
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author van Houwelingen, Hans C.
Putter, Hein
author_facet van Houwelingen, Hans C.
Putter, Hein
author_sort van Houwelingen, Hans C.
collection PubMed
description This paper considers the problem of obtaining a dynamic prediction for 5-year failure free survival after bone marrow transplantation in ALL patients using data from the EBMT, the European Group for Blood and Marrow Transplantation. The paper compares the new landmark methodology as developed by the first author and the established multi-state modeling as described in a recent Tutorial in Biostatistics in Statistics in Medicine by the second author and colleagues. As expected the two approaches give similar results. The landmark methodology does not need complex modeling and leads to easy prediction rules. On the other hand, it does not give the insight in the biological processes as obtained for the multi-state model.
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spelling pubmed-27980372010-01-13 Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data van Houwelingen, Hans C. Putter, Hein Lifetime Data Anal Article This paper considers the problem of obtaining a dynamic prediction for 5-year failure free survival after bone marrow transplantation in ALL patients using data from the EBMT, the European Group for Blood and Marrow Transplantation. The paper compares the new landmark methodology as developed by the first author and the established multi-state modeling as described in a recent Tutorial in Biostatistics in Statistics in Medicine by the second author and colleagues. As expected the two approaches give similar results. The landmark methodology does not need complex modeling and leads to easy prediction rules. On the other hand, it does not give the insight in the biological processes as obtained for the multi-state model. Springer US 2008-10-03 2008 /pmc/articles/PMC2798037/ /pubmed/18836831 http://dx.doi.org/10.1007/s10985-008-9099-8 Text en © Springer Science+Business Media, LLC 2008 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
spellingShingle Article
van Houwelingen, Hans C.
Putter, Hein
Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data
title Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data
title_full Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data
title_fullStr Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data
title_full_unstemmed Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data
title_short Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data
title_sort dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2798037/
https://www.ncbi.nlm.nih.gov/pubmed/18836831
http://dx.doi.org/10.1007/s10985-008-9099-8
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