title: Predictor characteristics necessary for building a clinically useful risk prediction model: a simulation study.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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 r2) for each model.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA. lauraschummers@mail.harvard.edu.
authors.forename: Laura
authors.identifier: http://orcid.org/0000-0003-3536-0498
authors.initials: L
authors.lastname: Schummers
authors.affiliation: Department of Epidemiology, Graduate School of Public Health, and Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh, A742 Crabtree Hall, 130 DeSoto Street, Pittsburgh, PA, 15261, USA.
authors.forename: Katherine P
authors.initials: KP
authors.lastname: Himes
authors.affiliation: Department of Obstetrics, Gynecology, and Reproductive Sciences, Magee-Womens Research Institute, University of Pittsburgh, 300 Halket Street, Pittsburgh, PA, 15213, USA.
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Bodnar
authors.affiliation: Department of Obstetrics & Gynaecology, University of British Columbia, 4500 Oak Street C408, Vancouver, British Columbia, V6H3N1, Canada.
authors.forename: Jennifer A
authors.initials: JA
authors.lastname: Hutcheon
keywords: Area under the receiver operating characteristic curve; Discrimination; Epidemiologic methods; Model performance; Risk classification; Risk prediction model