In:
Clinical Journal of the American Society of Nephrology, Ovid Technologies (Wolters Kluwer Health), Vol. 12, No. 6 ( 2017-6), p. 1010-1017
Kurzfassung:
Prediction models are often developed in and applied to CKD populations. These models can be used to inform patients and clinicians about the potential risks of disease development or progression. With increasing availability of large datasets from CKD cohorts, there is opportunity to develop better prediction models that will lead to more informed treatment decisions. It is important that prediction modeling be done using appropriate statistical methods to achieve the highest accuracy, while avoiding overfitting and poor calibration. In this paper, we review prediction modeling methods in general from model building to assessing model performance as well as the application to new patient populations. Throughout, the methods are illustrated using data from the Chronic Renal Insufficiency Cohort Study.
Materialart:
Online-Ressource
ISSN:
1555-9041
,
1555-905X
DOI:
10.2215/CJN.06210616
Sprache:
Englisch
Verlag:
Ovid Technologies (Wolters Kluwer Health)
Publikationsdatum:
2017
ZDB Id:
2216582-4