RiGoR: Reporting guidelines to address common sources of bias in risk model development

Kathleen F. Kerr, Allison Meisner, Heather Thiessen-Philbrook, Steven G. Coca, Chirag R. Parikh

Research output: Contribution to journalReview articlepeer-review

13 Scopus citations


Reviewing the literature in many fields on proposed risk models reveals problems with the way many risk models are developed. Furthermore, papers reporting new risk models do not always provide sufficient information to allow readers to assess the merits of the model. In this review, we discuss sources of bias that can arise in risk model development. We focus on two biases that can be introduced during data analysis. These two sources of bias are sometimes conflated in the literature and we recommend the terms resubstitution bias and model-selection bias to delineate them. We also propose the RiGoR reporting standard to improve transparency and clarity of published papers proposing new risk models.

Original languageEnglish (US)
Article number2
JournalBiomarker Research
Issue number1
StatePublished - Jan 24 2015
Externally publishedYes


  • Reporting standards
  • Research design
  • Risk prediction
  • Statistical bias

ASJC Scopus subject areas

  • Molecular Medicine
  • Clinical Biochemistry
  • Biochemistry, medical


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