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Activity Number:
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196
- SPEED: Biometrics and Biostatistics Part 2
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Type:
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Contributed
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Date/Time:
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Monday, July 29, 2019 : 11:35 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract #307595
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Title:
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A Scalable Algorithm for Joint Modeling of Longitudinal and Competing Risks Time-To-Event Data
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Author(s):
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Shanpeng Li* and Eric Kawaguchi and Gang Li
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Companies:
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UCLA Department of Biostatistics and UCLA Department of Biostatistics and UCLA
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Keywords:
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Joint modeling;
Longitudinal data;
Survival data;
Computational improvement
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Abstract:
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Joint modeling of longitudinal and time-to-event data is useful for longitudinal data analysis with possibly nonignorable missing data and for survival analysis with time-dependent covariates that are intermittently measured and/or with measurement errors. However, current estimation and inference methods for joint models are well known to be computationally complex and costly and do not scale well even to moderate sample size data. This work aims to improve the computational performance of joint modeling methods by developing novel techniques to exploit some specific structures in fitting a joint model. The developed new algorithm yields substantial speed ups over current methods. Numerical simulation results and real data illustrations will be presented.
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Authors who are presenting talks have a * after their name.
