Publication year
2019Publisher
Cham (CH) : Springer
Series
Fundamentals of Clinical Data Science
ISBN
9783319997124
In
Kubben, P.; Dumontier, M.; Dekker, A. (ed.), Fundamentals of Clinical Data Science, pp. 135-150Publication type
Part of book or chapter of book
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Editor(s)
Kubben, P.
Dumontier, M.
Dekker, A.
Organization
Radiation Oncology
Book title
Kubben, P.; Dumontier, M.; Dekker, A. (ed.), Fundamentals of Clinical Data Science
Page start
p. 135
Page end
p. 150
Subject
Fundamentals of Clinical Data Science; Radboudumc 9: Rare cancers RIHS: Radboud Institute for Health Sciences; Radiation Oncology - Radboud University Medical CenterAbstract
Prediction models have the potential to positively influence clinical decision-making and thus the overall quality of healthcare. The translational gap needs to be bridged between development of complex statistical models requiring multiple predictors and widespread usage in clinical consultation. A recent review found that inadequate quality of reporting of prediction modelling studies could be a contributing factor in slow transition to the clinic. This chapter emphasises the importance of high-quality reporting of modelling studies and the need for critical appraisal to understand the potential issues limiting generalizability of published models. Evidence synthesis (such as systematic reviews and pooled analysis of disparate models) are relatively under-represented in literature, though methodological studies and guidelines are now starting to appear.
This item appears in the following Collection(s)
- Academic publications [242594]
- Faculty of Medical Sciences [92290]
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