Document Detail


Poisson Growth Mixture Modeling of Intensive Longitudinal Data: An Application to Smoking Cessation Behavior.
MedLine Citation:
PMID:  22408365     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Intensive longitudinal data (ILD) have become increasingly common in the social and behavioral sciences; count variables, such as the number of daily smoked cigarettes, are frequently-used outcomes in many ILD studies. We demonstrate a generalized extension of growth mixture modeling (GMM) to Poisson-distributed ILD for identifying qualitatively distinct trajectories in the context of developmental heterogeneity in count data. Accounting for the Poisson outcome distribution is essential for correct model identification and estimation. In addition, setting up the model in a way that is conducive to ILD measures helps with data complexities - large data volume, missing observations, and differences in sampling frequency across individuals. We present technical details of model fitting, summarize an empirical example of patterns of smoking behavior change, and describe research questions the generalized GMM helps to address.
Authors:
Mariya P Shiyko; Yuelin Li; David Rindskopf
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Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  Structural equation modeling : a multidisciplinary journal     Volume:  19     ISSN:  1532-8007     ISO Abbreviation:  -     Publication Date:  2012 Jan 
Date Detail:
Created Date:  2012-3-12     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  100963000     Medline TA:  Struct Equ Modeling     Country:  -    
Other Details:
Languages:  ENG     Pagination:  65-85     Citation Subset:  -    
Affiliation:
The Methodology Center, The Pennsylvania State University, State College, PA.
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MeSH Terms
Descriptor/Qualifier:
Grant Support
ID/Acronym/Agency:
P50 DA010075-16//NIDA NIH HHS

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