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Activity Number:
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289
- Contributed Poster Presentations: Section on Statistics in Epidemiology
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Type:
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Contributed
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Date/Time:
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Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract #323040
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Title:
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Estimating Random Effects in a Finite Markov Chain with Absorbing States: Application to Cognitive Data
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Author(s):
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Pei Wang and Erin L Abner and Changrui Liu and David W Fardo and Frederick A Schmitt and Gregory A Jicha and Linda J Van Eldik and Richard Kryscio*
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Companies:
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Miami of Ohio and University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky
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Keywords:
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Markov chains;
multinomial logistic regression;
random effect;
marginal likelihood;
h-likelihood;
cognitive assessments
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Abstract:
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Finite Markov chains with absorbing states are popular tools for analyzing longitudinal data with categorical responses. The one step transition probabilities can be defined in terms of fixed and random effects but it is difficult to estimate these effects due to many unknown parameters. In this article we propose a three-step estimation method. In the first step the fixed effects are estimated by using a marginal likelihood function, in the second step the random effects are estimated after substituting the estimated fixed effects into a joint likelihood function defined as a h-likelihood, and in the third step the covariance matrix for the vector of random effects is estimated using the Hessian matrix for this likelihood function. An application involving an analysis of longitudinal cognitive data is used to illustrate the method
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Authors who are presenting talks have a * after their name.
