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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 #317491
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Title:
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Subsampling for the generalized estimating equation approach in the analysis of large-scale longitudinal data
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Author(s):
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Yujing Yao* and Zhezhen Jin
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Companies:
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Columbia University and Columbia University
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Keywords:
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Distributed algorithm;
Generalized estimating equation;
mHealth;
Perturbation;
Subsample
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Abstract:
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Large-scale longitudinal data are common nowadays due to technological development and collaborative research endeavors. One example is the use of mobile data collection app in healthcare studies, which yields a large sample size of longitudinal type mHealth data. In this paper, we propose a repeated perturbation subsampling for the analysis of large scale longitudinal data using generalized estimating equation(GEE). The GEE is a general approach for the analysis of longitudinal data by fitting marginal models. The method can provide consistent point estimator and variance estimator simultaneously. This method is also feasible for a distributed framework. We establish asymptotic properties of the resulting subsample estimators. We also illustrate the proposed method using the SleepHealth Mobile App Study (SHMAS) data.
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Authors who are presenting talks have a * after their name.
