TY - JOUR
T1 - Fracture Risk Prediction Modeling and Statistics
T2 - What Should Clinical Researchers, Journal Reviewers, and Clinicians Know?
AU - Schousboe, John T.
AU - Langsetmo, Lisa
AU - Taylor, Brent C
AU - Ensrud, Kristine E
PY - 2017/7
Y1 - 2017/7
N2 - Fractures are binary events (they either occur or they do not), and predicting whether fractures may occur involves assigning probabilities of one or more of those events occurring over time to populations and to individuals. Fracture risk prediction has become central to the management of osteoporosis and fracture prevention in clinical practice, and the ultimate clinical usefulness of the prediction tools used to estimate these risks depends, at a minimum, on the validity and accuracy of those tools. In this paper, we will describe how fracture prediction models are developed and validated, and how their performance characteristics are assessed. We will provide a checklist by which clinicians, clinical researchers, and reviewers of journal submissions can judge whether a fracture prediction tool meets basic requirements of good performance. We will further describe how the incremental predictive value of additional diagnostic tools, such as bone mass measurement technologies, is assessed.
AB - Fractures are binary events (they either occur or they do not), and predicting whether fractures may occur involves assigning probabilities of one or more of those events occurring over time to populations and to individuals. Fracture risk prediction has become central to the management of osteoporosis and fracture prevention in clinical practice, and the ultimate clinical usefulness of the prediction tools used to estimate these risks depends, at a minimum, on the validity and accuracy of those tools. In this paper, we will describe how fracture prediction models are developed and validated, and how their performance characteristics are assessed. We will provide a checklist by which clinicians, clinical researchers, and reviewers of journal submissions can judge whether a fracture prediction tool meets basic requirements of good performance. We will further describe how the incremental predictive value of additional diagnostic tools, such as bone mass measurement technologies, is assessed.
KW - Prediction modeling
KW - fractures
KW - model calibration
KW - model discrimination
KW - model validation
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U2 - 10.1016/j.jocd.2017.06.012
DO - 10.1016/j.jocd.2017.06.012
M3 - Article
C2 - 28712982
AN - SCOPUS:85023627483
SN - 1094-6950
VL - 20
SP - 280
EP - 290
JO - Journal of Clinical Densitometry
JF - Journal of Clinical Densitometry
IS - 3
ER -