How to establish clinical prediction models

Yong Ho Lee, Heejung Bang, Dae Jung Kim

Research output: Contribution to journalReview articlepeer-review

82 Scopus citations


A clinical prediction model can be applied to several challenging clinical scenarios: Screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: Preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice.

Original languageEnglish (US)
Pages (from-to)38-44
Number of pages7
JournalEndocrinology and Metabolism
Issue number1
StatePublished - Mar 1 2016


  • Clinical prediction model
  • Clinical usefulness
  • Development
  • Validation

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Endocrinology


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