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- Title
Landmark linear transformation model for dynamic prediction with application to a longitudinal cohort study of chronic disease.
- Authors
Zhu, Yayuan; Li, Liang; Huang, Xuelin
- Abstract
Summary: 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.
- Subjects
CHRONIC kidney failure; BIOLOGICAL tags; SURVIVAL analysis (Biometry); PROGNOSTIC tests; LINEAR equations
- Publication
Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, Vol 68, Issue 3, p771
- ISSN
0035-9254
- Publication type
Article
- DOI
10.1111/rssc.12334