Identifying and modelling prognostic factors with censored data
A major issue in the analysis of diseases is the identification and assessment of prognostic factors relevant to the development of the illness. Statistical analyses within the proportional hazards framework suffer from a lack of flexibility due to stringent model assumptions such as additivity and...
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Published in | Statistics in medicine Vol. 19; no. 4; pp. 601 - 615 |
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Main Authors | , , |
Format | Journal Article Conference Proceeding |
Language | English |
Published |
Chichester, UK
John Wiley & Sons, Ltd
29.02.2000
Wiley |
Subjects | |
Online Access | Get full text |
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Summary: | A major issue in the analysis of diseases is the identification and assessment of prognostic factors relevant to the development of the illness. Statistical analyses within the proportional hazards framework suffer from a lack of flexibility due to stringent model assumptions such as additivity and time‐constancy of effects. In this paper we use tree‐based models and varying coefficient models to allow for detectability of prognostic factors with possibly non‐additive, non‐linear and time‐varying impact on disease development. Questions concerning model and smoothing parameter selection are addressed. An analysis of a data set of breast cancer patients demonstrates the ability of these methods to reveal additional insight into the disease influencing mechanisms. Copyright © 2000 John Wiley & Sons, Ltd. |
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Bibliography: | ArticleID:SIM359 istex:D5D2DBBAD818995D0E4302F2D84080C51067D7C3 Deutsche Forschungsgemeinschaft - No. Sondesforschungsbereich 386 ark:/67375/WNG-G19XBHGG-C ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0277-6715 1097-0258 |
DOI: | 10.1002/(SICI)1097-0258(20000229)19:4<601::AID-SIM359>3.0.CO;2-K |