Statistical methods for cohort studies of CKD: Prediction modeling

Chronic Renal Insufficiency Cohort (CRIC) Study Investigators

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Prediction models are often developed in and applied to CKD populations. These models can be used to inform patients and clinicians about the potential risks of disease development or progression. With increasing availability of large datasets from CKD cohorts, there is opportunity to develop better prediction models that will lead to more informed treatment decisions. It is important that prediction modeling be done using appropriate statistical methods to achieve the highest accuracy, while avoiding overfitting and poor calibration. In this paper, we review prediction modeling methods in general from model building to assessing model performance as well as the application to new patient populations. Throughout, the methods are illustrated using data from the Chronic Renal Insufficiency Cohort Study.

Original languageEnglish (US)
Pages (from-to)1010-1017
Number of pages8
JournalClinical Journal of the American Society of Nephrology
Issue number6
StatePublished - 2017

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Critical Care and Intensive Care Medicine
  • Nephrology
  • Transplantation


  • C-statistic
  • Calibration
  • Cohort Studies
  • Disease Progression
  • Humans
  • ROC curve
  • Renal Insufficiency, Chronic
  • Risk
  • Sensitivity
  • Specificity


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