Dynamic Prediction of Renal Failure Using Longitudinal Biomarkers in a Cohort Study of Chronic Kidney Disease

Dynamic Prediction of Renal Failure Using Longitudinal Biomarkers in a Cohort Study of Chronic... In longitudinal studies, prognostic biomarkers are often measured longitudinally. It is of both scientific and clinical interest to predict the risk of clinical events, such as disease progression or death, using these longitudinal biomarkers as well as other time-dependent and time-independent information about the patient. The prediction is dynamic in the sense that it can be made at any time during the follow-up, adapting to the changing at-risk population and incorporating the most recent longitudinal data. One approach is to build a joint model of longitudinal predictor variables and time to the clinical event, and draw predictions from the posterior distribution of the time to event conditional on longitudinal history. Another approach is to use the landmark model, which is a system of prediction models that evolve with the follow-up time. We review the pros and cons of the two approaches and present a general analytical framework using the landmark approach. The proposed framework allows the measurement times of longitudinal data to be irregularly spaced and differ between subjects. We propose a unified kernel weighting approach for estimating the model parameters, calculating the predicted probabilities, and evaluating the prediction accuracy through double time-dependent receiver operating characteristic curves. We illustrate the proposed analytical framework using the African American study of kidney disease and hypertension to develop a landmark model for dynamic prediction of end-stage renal diseases or death among patients with chronic kidney disease. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistics in Biosciences Springer Journals

Dynamic Prediction of Renal Failure Using Longitudinal Biomarkers in a Cohort Study of Chronic Kidney Disease

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Publisher
Springer US
Copyright
Copyright © 2016 by International Chinese Statistical Association
Subject
Statistics; Statistics for Life Sciences, Medicine, Health Sciences; Biostatistics; Theoretical Ecology/Statistics
ISSN
1867-1764
eISSN
1867-1772
D.O.I.
10.1007/s12561-016-9183-7
Publisher site
See Article on Publisher Site

Abstract

In longitudinal studies, prognostic biomarkers are often measured longitudinally. It is of both scientific and clinical interest to predict the risk of clinical events, such as disease progression or death, using these longitudinal biomarkers as well as other time-dependent and time-independent information about the patient. The prediction is dynamic in the sense that it can be made at any time during the follow-up, adapting to the changing at-risk population and incorporating the most recent longitudinal data. One approach is to build a joint model of longitudinal predictor variables and time to the clinical event, and draw predictions from the posterior distribution of the time to event conditional on longitudinal history. Another approach is to use the landmark model, which is a system of prediction models that evolve with the follow-up time. We review the pros and cons of the two approaches and present a general analytical framework using the landmark approach. The proposed framework allows the measurement times of longitudinal data to be irregularly spaced and differ between subjects. We propose a unified kernel weighting approach for estimating the model parameters, calculating the predicted probabilities, and evaluating the prediction accuracy through double time-dependent receiver operating characteristic curves. We illustrate the proposed analytical framework using the African American study of kidney disease and hypertension to develop a landmark model for dynamic prediction of end-stage renal diseases or death among patients with chronic kidney disease.

Journal

Statistics in BiosciencesSpringer Journals

Published: Nov 7, 2016

References

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