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      Dynamic prediction of competing risk events using landmark sub-distribution hazard model with multiple longitudinal biomarkers.

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          Abstract

          The cause-specific cumulative incidence function quantifies the subject-specific disease risk with competing risk outcome. With longitudinally collected biomarker data, it is of interest to dynamically update the predicted cumulative incidence function by incorporating the most recent biomarker as well as the cumulating longitudinal history. Motivated by a longitudinal cohort study of chronic kidney disease, we propose a framework for dynamic prediction of end stage renal disease using multivariate longitudinal biomarkers, accounting for the competing risk of death. The proposed framework extends the local estimation-based landmark survival modeling to competing risks data, and implies that a distinct sub-distribution hazard regression model is defined at each biomarker measurement time. The model parameters, prediction horizon, longitudinal history and at-risk population are allowed to vary over the landmark time. When the measurement times of biomarkers are irregularly spaced, the predictor variable may not be observed at the time of prediction. Local polynomial is used to estimate the model parameters without explicitly imputing the predictor or modeling its longitudinal trajectory. The proposed model leads to simple interpretation of the regression coefficients and closed-form calculation of the predicted cumulative incidence function. The estimation and prediction can be implemented through standard statistical software with tractable computation. We conducted simulations to evaluate the performance of the estimation procedure and predictive accuracy. The methodology is illustrated with data from the African American Study of Kidney Disease and Hypertension.

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          Author and article information

          Journal
          Stat Methods Med Res
          Statistical methods in medical research
          SAGE Publications
          1477-0334
          0962-2802
          November 2020
          : 29
          : 11
          Affiliations
          [1 ] Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
          [2 ] Department of Biostatistics, University of Texas School of Public Health, Houston, TX, USA.
          Article
          10.1177/0962280220921553
          32419611
          080822d5-3a9e-4647-9814-9bec03844cb4
          History

          Competing risks,Fine-Gray model,dynamic prediction,landmark analysis,longitudinal biomarkers,prediction model

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