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Impact of correlation of predictors on discrimination of risk models in development and external populations

Overview of attention for article published in BMC Medical Research Methodology, April 2017
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Title
Impact of correlation of predictors on discrimination of risk models in development and external populations
Published in
BMC Medical Research Methodology, April 2017
DOI 10.1186/s12874-017-0345-1
Pubmed ID
Authors

Suman Kundu, Madhu Mazumdar, Bart Ferket

Abstract

The area under the ROC curve (AUC) of risk models is known to be influenced by differences in case-mix and effect size of predictors. The impact of heterogeneity in correlation among predictors has however been under investigated. We sought to evaluate how correlation among predictors affects the AUC in development and external populations. We simulated hypothetical populations using two different methods based on means, standard deviations, and correlation of two continuous predictors. In the first approach, the distribution and correlation of predictors were assumed for the total population. In the second approach, these parameters were modeled conditional on disease status. In both approaches, multivariable logistic regression models were fitted to predict disease risk in individuals. Each risk model developed in a population was validated in the remaining populations to investigate external validity. For both approaches, we observed that the magnitude of the AUC in the development and external populations depends on the correlation among predictors. Lower AUCs were estimated in scenarios of both strong positive and negative correlation, depending on the direction of predictor effects and the simulation method. However, when adjusted effect sizes of predictors were specified in the opposite directions, increasingly negative correlation consistently improved the AUC. AUCs in external validation populations were higher or lower than in the derivation cohort, even in the presence of similar predictor effects. Discrimination of risk prediction models should be assessed in various external populations with different correlation structures to make better inferences about model generalizability.

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 3 27%
Researcher 2 18%
Student > Postgraduate 2 18%
Student > Doctoral Student 1 9%
Professor 1 9%
Other 2 18%
Readers by discipline Count As %
Unspecified 4 36%
Medicine and Dentistry 4 36%
Mathematics 1 9%
Business, Management and Accounting 1 9%
Biochemistry, Genetics and Molecular Biology 1 9%
Other 0 0%