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Activity Number:
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116
- Epidemiological Models for Longitudinal Studies, Time-to-Event Outcomes, and Functional Data
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Type:
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Contributed
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Date/Time:
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Monday, August 8, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract #322180
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Title:
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Models and Methods for Analyzing Clustered Recurrent Hospitalizations in the Presence of COVID-19 Effects
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Author(s):
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Xuemei Ding* and Kevin He and Jack Kalbfleisch
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Companies:
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University of Michigan and University of Michigan and University of Michigan
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Keywords:
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Algorithm;
COVID-19 effects;
Large database;
Proportional rates;
Provider profiling;
Time-varying covariates
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Abstract:
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Recurrent events such as hospitalizations are outcomes that can be used to monitor facilities' quality of care. Current methods are not adequate to analyze data on many facilities with multiple hospitalizations. We propose a method that has a flexible baseline rate function and is computationally efficient. Additionally, the proposed method links indirect and direct standardization. The method is evaluated under a list of simulation settings and compared to the R package survival. Finally, we illustrate the methods with an important application to monitoring dialysis facilities in the United States, while making appropriate time dependent adjustments for the effects of COVID-19 infection.
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Authors who are presenting talks have a * after their name.
