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Research Article

Dynamic prediction by landmarking in competing risks

  1. M. A. Nicolaie1,*,
  2. J. C. van Houwelingen1,
  3. T. M. de Witte2,
  4. H. Putter1

Article first published online: 22 OCT 2012

DOI: 10.1002/sim.5665

Issue

Statistics in Medicine

Statistics in Medicine

Volume 32, Issue 12, pages 2031–2047, 30 May 2013

Additional Information

How to Cite

Nicolaie, M. A., van Houwelingen, J. C., de Witte, T. M. and Putter, H. (2013), Dynamic prediction by landmarking in competing risks. Statist. Med., 32: 2031–2047. doi: 10.1002/sim.5665

Author Information

  1. 1

    Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands

  2. 2

    Department of Haematology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands

* Correspondence to: M. A. Nicolaie, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands.
E-mail: M.A.Nicolaie@lumc.nl

Publication History

  1. Issue published online: 7 MAY 2013
  2. Article first published online: 22 OCT 2012
  3. Manuscript Accepted: 3 OCT 2012
  4. Manuscript Revised: 14 JUL 2012
  5. Manuscript Received: 16 FEB 2012

Funded by

  • Netherlands Organization for Scientific Research. Grant Number: ZONMW-912-07-018

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Keywords:

  • competing risks;
  • dynamic prediction;
  • time-dependent variables;
  • Cox proportional hazards model;
  • landmark

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 tLM within the follow-up interval. For each of these landmark time points tLM, we create a landmark data set by selecting individuals at risk at tLM; we fix the value of the time-dependent covariate in each landmark data set at tLM. 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.

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