Statistical Methods for Cohort Studies of CKD: Prediction Modeling

Clinical journal of the American Society of Nephrology: CJASN

Jason Roy³ 

[1] and..*Department of Biostatistics and Epidemiology and..;and..§Department of Medicine, University of California, San Francisco, California;*Department of Biostatistics and Epidemiology and..*Department of Biostatistics and Epidemiology and..*Department of Biostatistics and Epidemiology and..*Department of Biostatistics and Epidemiology and..*Department of Biostatistics and Epidemiology and..*Department of Biostatistics and Epidemiology and..*Department of Biostatistics and Epidemiology and..*Department of Biostatistics and Epidemiology and..‡Department of Epidemiology, Tulane University, New Orleans, Louisiana;§Department of Medicine, University of California, San Francisco, California

关键词: Calibration;    C-statistic;    ROC curve;    Sensitivity;    Specificity;    Cohort Studies;    Disease Progression;    Humans;    Risk;    Renal Insufficiency, Chronic;   

DOI  :  10.2215/CJN.06210616

学科分类：泌尿医学

来源: American Society of Nephrology

【 摘 要 】

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.

【 授权许可】

CC BY