How to Develop, Validate, and Compare Clinical Prediction Models Involving Radiological Parameters: Study Design and Statistical Methods

Korean J Radiol. May-Jun 2016;17(3):339-50. doi: 10.3348/kjr.2016.17.3.339. Epub 2016 Apr 14.

Abstract

Clinical prediction models are developed to calculate estimates of the probability of the presence/occurrence or future course of a particular prognostic or diagnostic outcome from multiple clinical or non-clinical parameters. Radiologic imaging techniques are being developed for accurate detection and early diagnosis of disease, which will eventually affect patient outcomes. Hence, results obtained by radiological means, especially diagnostic imaging, are frequently incorporated into a clinical prediction model as important predictive parameters, and the performance of the prediction model may improve in both diagnostic and prognostic settings. This article explains in a conceptual manner the overall process of developing and validating a clinical prediction model involving radiological parameters in relation to the study design and statistical methods. Collection of a raw dataset; selection of an appropriate statistical model; predictor selection; evaluation of model performance using a calibration plot, Hosmer-Lemeshow test and c-index; internal and external validation; comparison of different models using c-index, net reclassification improvement, and integrated discrimination improvement; and a method to create an easy-to-use prediction score system will be addressed. This article may serve as a practical methodological reference for clinical researchers.

Keywords: Diagnosis; Patient outcome; Prediction model; Prognosis.

Publication types

  • Review

MeSH terms

  • Area Under Curve
  • Coronary Artery Disease / diagnosis*
  • Coronary Artery Disease / diagnostic imaging
  • Coronary Artery Disease / mortality
  • Humans
  • Logistic Models
  • Models, Statistical*
  • Prognosis
  • Proportional Hazards Models
  • ROC Curve
  • Research Design
  • Survival Rate