Abstract Details
Activity Number:
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545
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract #311304
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View Presentation
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Title:
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Nonparametric Maximum Likelihood Estimators of Time-Dependent Accuracy Measures for Survival Outcome Under Two-Stage Sampling Designs
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Author(s):
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Dandan Liu*+ and Tianxi Cai and Lok Anna and Yingye Zheng
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Companies:
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Vanderbilt University and Harvard and University of Michigan and Fred Hutchinson Cancer Research Center
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Keywords:
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biomarker evaluation ;
survival outcome ;
two-stage sampling ;
time-dependent accuracy ;
case-cohort ;
nested-case-control
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Abstract:
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Two-phase study design becomes increasing popular for large prospective cohorts studies of rare chronic diseases such as cancer due to consideration of cost-efficiency. To account for the selection probabilities reflecting the sampling scheme of a specific design, the inverse probability-weighted (IPW) estimator are usually adopted. Although it is simple to implement, the most efficient approach is based on nonparametric maximum likelihood estimation (NPMLE). When time-to-event outcome is of interest, existing statistical methods focus on making efficient inference on relative hazard parameters from the Cox regression model. Recently, time-dependent accuracy measures for survival outcome evaluated at a pre-specified time given a threshold of the biomarker were developed. The corresponding IPW estimators were also proposed under the two-phase study design. In this paper, we study NPMLEs of time-dependent accuracy measures under the two-phase study design. The asymptotic results were derived and the proposed estimators were used in evaluation of a novel serum biomarker in predicting the diagnosis of hepatocellular carcinoma (HCC) under nested-case-control design.
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Authors who are presenting talks have a * after their name.
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