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Activity Number:
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53
- New Developments in Survival Analysis
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Type:
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Contributed
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Date/Time:
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Sunday, August 8, 2021 : 3:30 PM to 5:20 PM
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Sponsor:
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Biometrics Section
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Abstract #318444
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Title:
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Semiparametric Marginal Regression for Clustered Competing Risks Data with Missing Cause of Failure
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Author(s):
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Wenxian Zhou* and Giorgos Bakoyannis and Ying Zhang and Constantin Yiannoutsos
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Companies:
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Indiana University and Indiana University and University of Nebraska Medical Center and Indiana University
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Keywords:
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Clustered data;
Competing risks;
Informative cluster size;
Missing cause of failure;
Proportional cause-specific hazards;
Partial pseudolikelihood
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Abstract:
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Clustered competing risks data are commonly encountered in multicenter studies and are often complicated due to informative cluster size and missing causes of failure. To the best of our knowledge, there is no methodology proposed for population-averaged analysis for clustered competing risks data with informative cluster size and missing causes of failure. To address this problem, we consider the semiparametric marginal proportional cause-specific hazards model and propose a maximum partial pseudolikelihood estimator under a missing at random assumption. The proposed method does not impose assumptions regarding the within-cluster dependence and allows for informative cluster size. The asymptotic properties of the proposed estimators for both regression coefficients and infinite-dimensional parameters are rigorously established. Simulation studies show that the proposed method performs well and that methods that ignore the within-cluster dependence and the informative cluster size lead to invalid inferences. The proposed method is applied to competing risks data from a large multicenter HIV study where a significant portion of causes of failure is missing.
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Authors who are presenting talks have a * after their name.
