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Dynamic prediction by landmarking in competing risks
Author(s) -
Nicolaie M. A.,
Houwelingen J. C.,
Witte T. M.,
Putter H.
Publication year - 2012
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5665
Subject(s) - landmark , covariate , proportional hazards model , computer science , smoothing , data set , set (abstract data type) , hazard , data mining , statistics , interval (graph theory) , artificial intelligence , machine learning , mathematics , chemistry , organic chemistry , combinatorics , computer vision , programming language
We propose an extension of the landmark model for ordinary survival data as a new approach to the problem of dynamic prediction in competing risks with time‐dependent covariates. We fix a set of landmark time points t LM within the follow‐up interval. For each of these landmark time points t LM , we create a landmark data set by selecting individuals at risk at t LM ; we fix the value of the time‐dependent covariate in each landmark data set at t LM . We assume Cox proportional hazard models for the cause‐specific hazards and consider smoothing the (possibly) time‐dependent effect of the covariate for the different landmark data sets. Fitting this model is possible within the standard statistical software. We illustrate the features of the landmark modelling on a real data set on bone marrow transplantation. Copyright © 2012 John Wiley & Sons, Ltd.