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Lifetime Data Analysis

, Volume 25, Issue 1, pp 150–167 | Cite as

The Wally plot approach to assess the calibration of clinical prediction models

  • Paul BlancheEmail author
  • Thomas A. Gerds
  • Claus T. Ekstrøm
Article
  • 158 Downloads

Abstract

A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. Typically, the calibration assumption is assessed graphically but in practice it is often challenging to judge whether a “disappointing” calibration plot is the consequence of a departure from the calibration assumption, or alternatively just “bad luck” due to sampling variability. We propose a graphical approach which enables the visualization of how much a calibration plot agrees with the calibration assumption to address this issue. The approach is mainly based on the idea of generating new plots which mimic the available data under the calibration assumption. The method handles the common non-trivial situations in which the data contain censored observations and occurrences of competing events. This is done by building on ideas from constrained non-parametric maximum likelihood estimation methods. Two examples from large cohort data illustrate our proposal. The ‘wally’ R package is provided to make the methodology easily usable.

Keywords

Censoring Competing risks Model validation Prediction modeling Residual plot Survival analysis 

Notes

Acknowledgements

PB is grateful to the Bettencourt Schueller foundation for its support. We thank the DIVAT consortium and the Three-City study group for providing the data of the DIVAT and of the Three-City cohorts. Their supports are listed at www.divat.fr and www.three-city-study.com.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Paul Blanche
    • 1
    Email author
  • Thomas A. Gerds
    • 2
  • Claus T. Ekstrøm
    • 2
  1. 1.LMBAUniversity of South BrittanyVannesFrance
  2. 2.Department of biostatisticsUniversity of CopenhagenCopenhagenDenmark

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