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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.

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