%0 Journal Article %J Stat Med %D 2013 %T Using group-based latent class transition models to analyze chronic disability data from the National Long-Term Care Survey 1984-2004. %A White, Toby A %A Erosheva, Elena A %K Activities of Daily Living %K Aged %K Aged, 80 and over %K Disabled Persons %K Humans %K Models, Statistical %K Prevalence %K United States %X

Latent class transition models track how individuals move among latent classes through time, traditionally assuming a complete set of observations for each individual. In this paper, we develop group-based latent class transition models that allow for staggered entry and exit, common in surveys with rolling enrollment designs. Such models are conceptually similar to, but structurally distinct from, pattern mixture models of the missing data literature. We employ group-based latent class transition modeling to conduct an in-depth data analysis of recent trends in chronic disability among the U.S. elderly population. Using activities of daily living data from the National Long-Term Care Survey (NLTCS), 1982-2004, we estimate model parameters using the expectation-maximization algorithm, implemented in SAS PROC IML. Our findings indicate that declines in chronic disability prevalence, observed in the 1980s and 1990s, did not continue in the early 2000s as previous NLTCS cross-sectional analyses have indicated.

%B Stat Med %V 32 %P 3569-89 %8 2013 Sep 10 %G eng %N 20 %R 10.1002/sim.5782 %0 Journal Article %J Proc Natl Acad Sci U S A %D 2010 %T Reconceptualizing the classification of PNAS articles. %A Airoldi, Edoardo M %A Erosheva, Elena A %A Fienberg, Stephen E %A Joutard, Cyrille %A Love, Tanzy %A Shringarpure, Suyash %K Classification %K Methods %K National Academy of Sciences (U.S.) %K Periodicals as Topic %K Publications %K Statistics as Topic %K United States %X

PNAS article classification is rooted in long-standing disciplinary divisions that do not necessarily reflect the structure of modern scientific research. We reevaluate that structure using latent pattern models from statistical machine learning, also known as mixed-membership models, that identify semantic structure in co-occurrence of words in the abstracts and references. Our findings suggest that the latent dimensionality of patterns underlying PNAS research articles in the Biological Sciences is only slightly larger than the number of categories currently in use, but it differs substantially in the content of the categories. Further, the number of articles that are listed under multiple categories is only a small fraction of what it should be. These findings together with the sensitivity analyses suggest ways to reconceptualize the organization of papers published in PNAS.

%B Proc Natl Acad Sci U S A %V 107 %P 20899-904 %8 2010 Dec 7 %G eng %N 49 %R 10.1073/pnas.1013452107