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Activity Number: 135 - Novel Non/Semiparametric Developments for Risk Perception with Censored and/or Missing Data
Type: Invited
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Risk Analysis
Abstract #322166
Title: Risk Prediction from the Combination of Longitudinal Biomarkers Subject to Informative Missingness
Author(s): Jiwei Zhao*
Companies: State University of New York at Buffalo
Keywords: risk prediction ; longitudinal biomarkers ; informative missingness ; linear mixed effects model ; pattern mixture model ; missing data mechanism

Diagnostic studies often focus on the use of the combination of longitudinal biomarkers for predicting a subsequent binary disease status or its risk. Given the disease status, the longitudinal biomarkers are usually statistically handled in a linear mixed effects model. However, it is the rule rather than the exception that these longitudinal data suffer from missing values and/or dropout, furthermore, its underlying mechanism is usually informative, or in general, complex and even unverifiable. The motivation of our work is to impose a less restrictive, hence more flexible, missing data mechanism for the longitudinal biomarkers in predicting a binary event. Under this generally applicable missing data mechanism, we introduce two approaches to estimate the unknown parameters in the linear mixed effects model for the longitudinal biomarkers. Afterwards, we propose to compute the test statistic and use it as the combination rule. We also derive the individual disease risk score and its confidence interval. We conduct simulation studies to illustrate our method and also apply it to a real data analysis.

Authors who are presenting talks have a * after their name.

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