Evaluating prediction performance of longitudinal biomarkers under cohort and two-phase study designs
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Risk prediction and evaluation of predictions based on longitudinal biomarkers are of interest in treatment selection, preventive medicine and management of chronic diseases. Methods to evaluate risk predictions in a longitudinal setting are limited to the area under the receiver operating characteristic curves and prediction error. In this dissertation, we evaluate two approaches to risk prediction in the longitudinal setting: joint modeling and partly conditional modeling. We develop estimation procedures for more flexible and robust partly conditional models, demonstrate their adaptability and applicability, and provide a smoothing technique to account for measurement error in marker data. We develop nonparametric estimators of clinically relevant measures of prediction quality in the longitudinal setting under cohort, case-cohort, stratified case-cohort and nested case-control study designs. We provide resampling-based inference procedures for all estimators under the four study designs. We evaluate our methods using simulation studies and illustrate them on the End Stage Renal Disease Study dataset and a nested case-control study within the HALT-C clinical trial.
- Biostatistics