PROBAST: a 22 item tool to assess the risk of bias of studies on prediction models, and to assess their applicability for the targeted context and population.

Diagnostic & prognostic prediction models

A prediction model is defined as any form of mathematical equation that combines two or more predictors to estimate the probability that a certain outcome is currently present diagnostic prediction model – or will occur in some time period – prognostic prediction model.

Types of prediction model studies

PROBAST helps assessing studies on multivariable prediction models that are used to make predictions in individuals, that is individualised predictions, including studies on:

  • development of new prediction models;
  • development and validation of same prediction model(s);
  • validation existing prediction models;
  • development of new prediction model compared with validation of existing prediction models;
  • updating or extension of existing prediction models;
  • combination of any of the above.

Types of predictors, outcomes and modelling technique

PROBAST can be used to assess any type of diagnostic or prognostic prediction model used for individualized predictions, regardless of the type of:

  • predictors; for example a demographic, a clinical, a biomarker, an imaging or omics;
  • outcomes being predicted; for example binary, time-to-event, linear;
  • statistical method used; for example logistic, survival, machine or deep learning techniques.