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Predictor characteristics necessary for building a clinically useful risk prediction model: a simulation study

Overview of attention for article published in BMC Medical Research Methodology, September 2016
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Title
Predictor characteristics necessary for building a clinically useful risk prediction model: a simulation study
Published in
BMC Medical Research Methodology, September 2016
DOI 10.1186/s12874-016-0223-2
Pubmed ID
Authors

Laura Schummers, Katherine P. Himes, Lisa M. Bodnar, Jennifer A. Hutcheon

Abstract

Compelled by the intuitive appeal of predicting each individual patient's risk of an outcome, there is a growing interest in risk prediction models. While the statistical methods used to build prediction models are increasingly well understood, the literature offers little insight to researchers seeking to gauge a priori whether a prediction model is likely to perform well for their particular research question. The objective of this study was to inform the development of new risk prediction models by evaluating model performance under a wide range of predictor characteristics. Data from all births to overweight or obese women in British Columbia, Canada from 2004 to 2012 (n = 75,225) were used to build a risk prediction model for preeclampsia. The data were then augmented with simulated predictors of the outcome with pre-set prevalence values and univariable odds ratios. We built 120 risk prediction models that included known demographic and clinical predictors, and one, three, or five of the simulated variables. Finally, we evaluated standard model performance criteria (discrimination, risk stratification capacity, calibration, and Nagelkerke's r(2)) for each model. Findings from our models built with simulated predictors demonstrated the predictor characteristics required for a risk prediction model to adequately discriminate cases from non-cases and to adequately classify patients into clinically distinct risk groups. Several predictor characteristics can yield well performing risk prediction models; however, these characteristics are not typical of predictor-outcome relationships in many population-based or clinical data sets. Novel predictors must be both strongly associated with the outcome and prevalent in the population to be useful for clinical prediction modeling (e.g., one predictor with prevalence ≥20 % and odds ratio ≥8, or 3 predictors with prevalence ≥10 % and odds ratios ≥4). Area under the receiver operating characteristic curve values of >0.8 were necessary to achieve reasonable risk stratification capacity. Our findings provide a guide for researchers to estimate the expected performance of a prediction model before a model has been built based on the characteristics of available predictors.

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Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 37 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 16%
Student > Bachelor 6 16%
Student > Ph. D. Student 5 13%
Student > Master 4 11%
Student > Postgraduate 3 8%
Other 7 18%
Unknown 7 18%
Readers by discipline Count As %
Medicine and Dentistry 12 32%
Nursing and Health Professions 3 8%
Biochemistry, Genetics and Molecular Biology 2 5%
Mathematics 1 3%
Agricultural and Biological Sciences 1 3%
Other 7 18%
Unknown 12 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 July 2020.
All research outputs
#15,384,989
of 22,889,074 outputs
Outputs from BMC Medical Research Methodology
#1,514
of 2,024 outputs
Outputs of similar age
#202,686
of 320,659 outputs
Outputs of similar age from BMC Medical Research Methodology
#32
of 45 outputs
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