Evaluating the Predictive Value of Biomarkers with Stratified Case‐Cohort Design
From MaRDI portal
Publication:4911947
DOI10.1111/j.1541-0420.2012.01787.xzbMath1274.62823OpenAlexW2123221938WikidataQ37032279 ScholiaQ37032279MaRDI QIDQ4911947
Tianxi Cai, Yingye Zheng, Dandan Liu
Publication date: 20 March 2013
Published in: Biometrics (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc3718317
survival analysisrisk predictiontwo-phase studycase-cohort samplingnegative predictive valuespositive predictive valuesreceiver operating characteristics curve (ROC curve)integrated discrimination improvement (IDI)
Related Items
Regularized regression for two phase failure time studies, Kernel machine testing for risk prediction with stratified case cohort studies, Estimation of covariate-specific time-dependent ROC curves in the presence of missing biomarkers, Nonparametric Maximum Likelihood Estimators of Time-Dependent Accuracy Measures for Survival Outcome Under Two-Stage Sampling Designs
Cites Work
- Unnamed Item
- Unnamed Item
- Weighted analyses for cohort sampling designs
- Smoothed Cox regression
- Exposure stratified case-cohort designs
- Time-Dependent Predictive Values of Prognostic Biomarkers With Failure Time Outcome
- A case-cohort design for epidemiologic cohort studies and disease prevention trials
- Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
- Nonparametric Survival Estimation Using Prognostic Longitudinal Covariates
- Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker
- On fitting Cox's proportional hazards models to survey data
- Weighted Likelihood for Semiparametric Models and Two‐phase Stratified Samples, with Application to Cox Regression
- Quantifying and comparing the predictive accuracy of continuous prognostic factors for binary outcomes
- Survival Model Predictive Accuracy and ROC Curves
- Weighted Estimators for Proportional Hazards Regression With Missing Covariates