Activity Number:
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362
- Contributed Poster Presentations: Mental Health Statistics Section
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Mental Health Statistics Section
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Abstract #330545
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Title:
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Models for Repeated Clustered Data with Informative Cluster Sizes with Applications in Psychiatry
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Author(s):
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Ana-Maria Iosif* and Laura M Tully and Tara A Niendam
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Companies:
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University of California, Davis and University of California Davis and University of California Davis
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Keywords:
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informative cluster size;
clustered data;
recurring episodes;
psychosis
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Abstract:
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Many psychiatric conditions manifest with recurring episodes, each of which can be characterized by a measure of intensity or severity. Both the number of episodes and the severity of each episode can depend on the latent severity of an individual's underlying condition. Furthermore, the underlying condition severity might be impacted by observed risk factors. In psychosis, stressful social interactions such as interpersonal conflicts predict symptom exacerbation. Data like this are commonly collected for a broad range of chronic conditions for which repeated data are available on both the number of episodes and their severity. From a clinical standpoint it is important to assess both these outcomes, since they both might inform about the condition severity. We propose models to analyze data collected repeatedly on both the frequency of an event and its severity when both of these are informative about the underlying latent severity. We use a ML framework that allows the use of covariates and derive estimators with good asymptotic and finite sample properties for sample sizes in a small to moderate range. We implement this method to data examining symptom exacerbation in psychosis.
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Authors who are presenting talks have a * after their name.