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Activity Number:
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137
- Joint Modeling for Longitudinal and Survival Outcomes in Health Studies
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 8, 2022 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #322719
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Title:
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Joint Modeling in Presence of Informative Censoring in Palliative Care Studies
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Author(s):
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Quran Wu and Michael Daniels and Zhigang Li*
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Companies:
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University of Florida and University of Florida and University of Florida
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Keywords:
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Frailty model;
Palliative care;
Semi-parametric;
Joint modeling
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Abstract:
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Joint modeling of longitudinal data such as quality of life data and survival data is important for palliative care researchers to draw efficient inference because it can account for the associations between those two types of data. Modeling quality of life on a retrospective time scale from death time makes it convenient for investigators to interpret the analysis results of palliative care studies with relatively short life expectancy. However, censoring of death times, especially informative censoring such as informative dropouts, poses challenges for modeling quality of life on a retrospective time scale. We develop a novel joint modeling approach that can address the challenge by allowing informative censoring events to be dependent on patients' quality of life through a random effect. Model performance is assessed with a simulation study in comparison with existing approaches. A real-world study is presented to showcase the application of the new approach.
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Authors who are presenting talks have a * after their name.
