Risk prediction models: A framework for assessment
Background: Medical risk prediction models estimate the likelihood of future health-related events. Many make use of information derived from analysis of the genome. Models predict health outcomes such as cardiovascular disease, stroke and cancer, and for some conditions several models exist. Although risk models can help decision-making in clinical medicine and public health, they can also be harmful, for example, by misdirecting clinical effort away from those who are most likely to benefit towards people with less need, thus exacerbating health inequalities. Discussion: Risk prediction models need careful assessment before implementation, but the current approach to their development, evaluation and implementation is inappropriate. As a result, some models are pressed into use before it is clear whether they are suitable, while in other cases there is confusion about which model to use. This paper proposes an approach to the appraisal of risk-scoring models, based on a conference of UK experts. Summary: By specifying what needs to be known before a model can be judged suitable for translation from research into practice, we can ensure that useful models are taken up promptly, that less well-proven ones undergo further evaluation and that resources are not wasted on ineffective ones.
|Keywords||Assessment, Evaluation, Genetic, Risk prediction|
|Persistent URL||dx.doi.org/10.1159/000334436, hdl.handle.net/1765/31947|
Dent, T.H.S., Wright, C.F., Stephan, B.C.M., Brayne, C., & Janssens, A.C.J.W.. (2012). Risk prediction models: A framework for assessment. Public Health Genomics, 15(2), 98–105. doi:10.1159/000334436