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Landmark proportional subdistribution hazards models for dynamic prediction of cumulative incidence functions

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Listed:
  • Qing Liu
  • Gong Tang
  • Joseph P. Costantino
  • Chung‐Chou H. Chang

Abstract

An individualized dynamic risk prediction model that incorporates all available information collected over the follow‐up can be used to choose an optimal treatment strategy in realtime, although existing methods have not been designed to handle competing risks. In this study, we developed a landmark proportional subdistribution hazard (PSH) model and a comprehensive supermodel for dynamic risk prediction with competing risks. Simulations showed that our proposed models perform satisfactorily (assessed by the time‐dependent relative difference, Brier score and area under the receiver operating characteristics curve) under PSH or non‐PSH settings. The models were used to predict the probabilities of developing a distant metastasis among breast cancer patients where death was treated as a competing risk. Prediction can be estimated by using standard statistical packages.

Suggested Citation

  • Qing Liu & Gong Tang & Joseph P. Costantino & Chung‐Chou H. Chang, 2020. "Landmark proportional subdistribution hazards models for dynamic prediction of cumulative incidence functions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1145-1162, November.
    • Handle: RePEc:bla:jorssc:v:69:y:2020:i:5:p:1145-1162
    DOI: 10.1111/rssc.12433
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    References listed on IDEAS

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