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Activity Number:
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122
- Survival Analysis: New Models and Methods
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Type:
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Contributed
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Date/Time:
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Monday, July 31, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract #324192
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View Presentation
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Title:
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Generalized Accelerated Recurrence Time Model for Multivariate Recurrent Event Data with Missing Event Type
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Author(s):
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Huijuan Ma* and Limin Peng and Zhumin Zhang and HuiChuan J. Lai
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Companies:
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and Emory University and University of Wisconsin-Madison and University of Wisconsin-Madison
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Keywords:
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Accelerated recurrence time model ;
Nadaraya--Watson kernel estimator ;
Multivariate recurrent event data ;
Missing at random
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Abstract:
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Multivariate recurrent event data are frequently encountered in biomedical and epidemiological studies when subjects experience multiple types of recurrent events. In practice, the event type information may be missing due to a variety of reasons. In this paper, we investigate the generalized accelerated recurrence time model for multivariate recurrent event data with missing event types. We propose methods that utilize the nonparametric inverse probability weighting technique or the estimating equation projection strategy to handle event types that are missing at random. Our proposal do not require imposing any parametric model for the missing mechanism. We establish the uniform consistency and weak convergence of the resulting estimators and develop appropriate inferential procedures. Numerous simulation studies and an application to a Cystic Fibrosis Foundation Patient Registry (CFFPR) dataset illustrate the validity and practical utility of the proposed methods.
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Authors who are presenting talks have a * after their name.
