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Activity Number:
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74
- Developments in Epidemiologic Models
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Type:
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Contributed
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Date/Time:
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Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract #324184
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Title:
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A Joint Logistic Regression and Covariate-Adjusted Continuous-Time Markov Chain Model with Application in Predicting 6-Month Outcome After Traumatic Brain Injury
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Author(s):
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Maria Laura Rubin* and Wenyaw Chan and Jose-Miguel Yamal and Claudia Sue Robertson
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Companies:
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Department of Biostatistics, University of Texas Health Science Center at Houston and University of Texas Health Science Center at Houston School of Public Health and University of Texas Health Science Center School of Public Health and Department of Neurosurgery, Baylor College of Medicine
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Keywords:
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Continuous-time Markov chain ;
Logistic regression ;
Longitudinal data ;
Joint model ;
Transition rates
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Abstract:
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Joint models are commonly used to describe two or more models simultaneously by considering the correlated nature of their outcomes and the random error present in the longitudinal measurements. There is limited research on joint models with longitudinal predictors and categorical cross-sectional outcomes. Perhaps the most challenging task is how to model the longitudinal predictor process such that it represents the true biological mechanism that dictates the association with the categorical response. We propose a joint logistic regression and Markov chain model to describe a binary cross-sectional response, where the unobserved transition rates of a two-state continuous-time Markov chain are included as covariates. A simulation study shows that our estimation method is adequate. We apply the proposed joint model to a dataset of patients with traumatic brain injury to describe and predict a 6-month outcome based on physiological data collected post-injury and admission characteristics. Our analysis indicates that the information provided by physiological changes over time may help improve prediction of long-term functional status of these severely ill subjects.
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Authors who are presenting talks have a * after their name.
