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Activity Number:
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275
- Joint Models for Complex Data: An Update on Computational Issues, Solutions, and Applications
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Type:
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Topic-Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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International Chinese Statistical Association
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Abstract #317625
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Title:
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Semiparametric Regression of the Illness-Death Model with Interval Censored Disease Incidence Time: an Application to the ACLS Data
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Author(s):
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Jiajia Zhang* and Jie Zhou and Alexander McLain and Wenbin Lu and Xuemei Sui and James Hardin
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Companies:
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University of South Carolina and University of South Carolina and University of South Carolina and North Carolina State University and University of South Carolina and University of South Carolina
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Keywords:
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Semi-competing model;
Ill-ness death model;
Semiparametric regression;
Interval censoring;
Markov models
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Abstract:
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To investigate the effect of fitness on cardiovascular disease (CVD) and all-cause mortality using the Aerobics Center Longitudinal Study (ACLS), we develop a semiparametric illness-death model account for intermittent observations of the CVD incidence time and the right censored data of all-cause mortality. The main challenge in estimation is to handle the intermittent observations (interval censoring) of CVD incidence time and we develop a semiparametric estimation method based on the expectation-maximization (EM) algorithm for a Markov illness-death regression model. The variance of the parameters are estimated using profile likelihood methods. The proposed method is evaluated using extensive simulation studies and illustrated with an application to the ACLS data.
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Authors who are presenting talks have a * after their name.
