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Activity Number: 231 - Biopharmaceutical Section Student Papers
Type: Contributed
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #323261 View Presentation
Title: Joint Analysis of Left-Censored Longitudinal Biomarker and Binary Outcome via Latent Class Modeling
Author(s): Menghan Li* and Lan Kong
Companies: Pennsylvania State University and Pennsylvania State University
Keywords: longitudinal biomarker ; limit of detection ; joint modeling ; Monte Carlo EM
Abstract:

Joint latent class modeling is an appealing approach for evaluating the association between a longitudinal biomarker and clinical outcome when the study population is heterogeneous. The link between the biomarker trajectory and the risk of event is reflected by the latent classes, which accommodate the underlying population heterogeneity. The estimation of joint latent class models may be complicated by the censored data in the biomarker measurements due to detection limits.We propose a modified likelihood function under the parametric assumption of biomarker distribution, and develop a Monte Carlo EM (MCEM) algorithm for joint analysis of a biomarker and a binary outcome. We conduct simulation studies to demonstrate the satisfactory performance of our MCEM algorithm and the superiority of our method to the naive imputation method for handling censored biomarker data. In addition, we apply our method to the Genetic and Inflammatory Markers of Sepsis (GenIMS) study to investigate the role of inflammatory biomarker profile in predicting 90-day mortality for patients hospitalized with community-acquired pneumonia.


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

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