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Landmark linear transformation model for dynamic prediction with application to a longitudinal cohort study of chronic disease

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  • Yayuan Zhu
  • Liang Li
  • Xuelin Huang

Abstract

Dynamic prediction of the risk of a clinical event by using longitudinally measured biomarkers or other prognostic information is important in clinical practice. We propose a new class of landmark survival models. The model takes the form of a linear transformation model but allows all the model parameters to vary with the landmark time. This model includes many published landmark prediction models as special cases. We propose a unified local linear estimation framework to estimate time varying model parameters. Simulation studies are conducted to evaluate the finite sample performance of the method proposed. We apply the methodology to a data set from the African American Study of Kidney Disease and Hypertension and predict individual patients’ risk of an adverse clinical event.

Suggested Citation

  • Yayuan Zhu & Liang Li & Xuelin Huang, 2019. "Landmark linear transformation model for dynamic prediction with application to a longitudinal cohort study of chronic disease," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(3), pages 771-791, April.
  • Handle: RePEc:bla:jorssc:v:68:y:2019:i:3:p:771-791
    DOI: 10.1111/rssc.12334
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