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Activity Number:
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32
- Longitudinal/Correlated Data I
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract #328919
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Presentation
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Title:
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Rank-Tracking Probabilities of Bivariate Dependent Variables in Longitudinal Studies
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Author(s):
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Seonjin Kim* and Hyunkeun Cho and Colin O. Wu
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Companies:
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Miami University and University of Iowa and National Heart, Lung and Blood Institute, NIH
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Keywords:
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Bivariate response;
Conditional distribution;
Longitudinal data;
Kernel estimation;
Tracking ability
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Abstract:
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We proposed a novel methodology to estimate rank-tracking probability for bivariate response vector. The rank-tracking probability can evaluate the risk of future disease based on an individuals' current health condition and thus proper intervention can be provided to reduce the risk. A quantile regression based approach is adopted to estimate the conditional distribution of bivariate response variable and the rank-tracking probability. National Growth and Health Study data is analyzed using the proposed procedure to compute the probability that a preadolescent ten-year-old girl will be diagnosed with hypertension at age 18 (late adolescence), based on characteristics she exhibits at ten years old, including her BMI, height, race and cardiovascular conditions.
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Authors who are presenting talks have a * after their name.
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