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A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis.

Publication ,  Journal Article
Goldstein, BA; Pomann, GM; Winkelmayer, WC; Pencina, MJ
Published in: Stat Med
July 30, 2017

An increasingly important data source for the development of clinical risk prediction models is electronic health records (EHRs). One of their key advantages is that they contain data on many individuals collected over time. This allows one to incorporate more clinical information into a risk model. However, traditional methods for developing risk models are not well suited to these irregularly collected clinical covariates. In this paper, we compare a range of approaches for using longitudinal predictors in a clinical risk model. Using data from an EHR for patients undergoing hemodialysis, we incorporate five different clinical predictors into a risk model for patient mortality. We consider different approaches for treating the repeated measurements including use of summary statistics, machine learning methods, functional data analysis, and joint models. We follow up our empirical findings with a simulation study. Overall, our results suggest that simple approaches perform just as well, if not better, than more complex analytic approaches. These results have important implication for development of risk prediction models with EHRs. Copyright © 2017 John Wiley & Sons, Ltd.

Duke Scholars

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

July 30, 2017

Volume

36

Issue

17

Start / End Page

2750 / 2763

Location

England

Related Subject Headings

  • Survival Analysis
  • Statistics & Probability
  • Risk Assessment
  • Renal Dialysis
  • Monte Carlo Method
  • Humans
  • Electronic Health Records
  • Computer Simulation
  • Biometry
  • Algorithms
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Goldstein, B. A., Pomann, G. M., Winkelmayer, W. C., & Pencina, M. J. (2017). A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis. Stat Med, 36(17), 2750–2763. https://doi.org/10.1002/sim.7308
Goldstein, Benjamin A., Gina Maria Pomann, Wolfgang C. Winkelmayer, and Michael J. Pencina. “A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis.Stat Med 36, no. 17 (July 30, 2017): 2750–63. https://doi.org/10.1002/sim.7308.
Goldstein BA, Pomann GM, Winkelmayer WC, Pencina MJ. A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis. Stat Med. 2017 Jul 30;36(17):2750–63.
Goldstein, Benjamin A., et al. “A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis.Stat Med, vol. 36, no. 17, July 2017, pp. 2750–63. Pubmed, doi:10.1002/sim.7308.
Goldstein BA, Pomann GM, Winkelmayer WC, Pencina MJ. A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis. Stat Med. 2017 Jul 30;36(17):2750–2763.
Journal cover image

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

July 30, 2017

Volume

36

Issue

17

Start / End Page

2750 / 2763

Location

England

Related Subject Headings

  • Survival Analysis
  • Statistics & Probability
  • Risk Assessment
  • Renal Dialysis
  • Monte Carlo Method
  • Humans
  • Electronic Health Records
  • Computer Simulation
  • Biometry
  • Algorithms