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Title page for ETD etd-050605-155002


Document Typethesis
Author NameBai, Yan
URNetd-050605-155002
TitleA Bayesian Approach to Detect the Onset of Activity Limitation Among Adults in NHIS
DegreeMS
DepartmentMathematical Sciences
Advisors
  • Balgobin Nandram, Advisor
  • Bodgan Vernescu, Department Head
  • Keywords
  • Change point
  • Gibbs sampler
  • Hierarchical Bayesian model
  • Reversible jump
  • Date of Presentation/Defense2005-05-06
    Availability unrestricted

    Abstract

    Data from the 1995 National Health Interview Survey (NHIS) indicate that, due to chronic conditions, the onset of activity limitation typically occurs between age 40-70 years (i.e., the proportion of young adults with activity limitation is small and roughly constant with age and then it starts to change, roughly increasing). We use a Bayesian hierarchical model to detect the change point of a positive activity limitation status (ALS) across twelve domains based on race, gender, and education. We have two types of data: weighted and unweighted. We obtain weighted binomial counts using a regression analysis with the sample weights. Given the proportion of individuals in the population with positive ALS, we assume that the number of individuals with positive ALS at each age group has a binomial probability mass function. The proportions across age are different, and have the same beta distribution up to the change point (unknown), and the proportions after the change point have a different beta distribution.

    We consider two different analyses. The first considers each domain individually in its own model and the second considers the twelve domains simultaneously in a single model to “borrow strength” as in small area estimation. It is reasonable to assume that each domain has its own onset.In the first analysis, we use the Gibbs sampler to fit the model, and a computation of the marginal likelihoods, using an output analysis from the Gibbs sampler, provides the posterior distribution of the change point. We note that a reversible jump sampler fails in this analysis because it tends to get stuck either age 40 or age 70. In the second analysis, we use the Gibbs sampler to fit only the joint posterior distribution of the twelve change points. This is a difficult problem because the joint density requires the numerical computation of a triple integral at each iteration. The other parameters of the process are obtained using data augmentation by a Metropolis sampler and a Rao-Blackwellization.

    We found that overall the age of onset is about 50 to 60 years.

    Files
  • onset.pdf

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