title: Statistical Methods for Cohort Studies of CKD: Prediction Modeling.
abstract: 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.
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Jason
authors.initials: J
authors.lastname: Roy
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Haochang
authors.initials: H
authors.lastname: Shou
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Dawei
authors.initials: D
authors.lastname: Xie
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Jesse Y
authors.initials: JY
authors.lastname: Hsu
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Wei
authors.initials: W
authors.lastname: Yang
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Amanda H
authors.initials: AH
authors.lastname: Anderson
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: J Richard
authors.initials: JR
authors.lastname: Landis
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Christopher
authors.initials: C
authors.lastname: Jepson
authors.affiliation: Department of Epidemiology, Tulane University, New Orleans, Louisiana.
authors.forename: Jiang
authors.initials: J
authors.lastname: He
authors.affiliation: Department of Medicine, University of California, San Francisco, California; and.
authors.forename: Kathleen D
authors.initials: KD
authors.lastname: Liu
authors.affiliation: Department of Medicine, University of California, San Francisco, California; and.|Division of Research, Kaiser Permanente Northern California, Oakland, California.
authors.forename: Chi-Yuan
authors.initials: CY
authors.lastname: Hsu
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Harold I
authors.initials: HI
authors.lastname: Feldman
mesh_terms: D035843:Biomedical Research / Q000706:statistics & numerical data*; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D009398:Nephrology / Q000706:statistics & numerical data*; D011379:Prognosis; D051436:Renal Insufficiency, Chronic / Q000175:diagnosis / Q000453:epidemiology / Q000628:therapy; D015203:Reproducibility of Results
keywords: C-statistic; Calibration; Cohort Studies; Disease Progression; Humans; ROC curve; Renal Insufficiency, Chronic; Risk; Sensitivity; Specificity