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Liu, Shelley H.

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Liu

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Shelley H.

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Liu, Shelley H.

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2015 - 20162

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    Statistical Methods for Estimating the Effects of Multi-Pollutant Exposures in Children's Health Research
    (2016-09-15) Liu, Shelley H.; Coull, Brent; Lin, Xihong; Wright, Robert
    We develop statistical strategies to explore how time-varying exposures to heavy metal mixtures affects cognition and cognitive trajectories in children. In chapter 1, we develop a Bayesian model, called Lagged Kernel Machine Regression (LKMR), to identify time windows of susceptibility to exposures of metal mixtures. In chapter 2, we develop a Mean Field Variational Bayesian (MFVB) inference procedure for LKMR. We demonstrate large computational gains under MFVB as opposed to Markov chain Monte Carlo (MCMC) inference for LKMR, which allows for the analysis of large datasets while maintaining accuracy. In chapter 3, we present a Bayesian hierarchical model, called Bayesian Varying Coefficient Kernel Machine Regression, to investigate the impact of exposure to heavy metal mixtures on cognitive growth trajectories in children. Simulation studies demonstrate the effectiveness of these methods, and the methods are used to analyze data from two prospective birth cohort studies in Mexico City.
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    Viral Genetic Linkage Analysis in the Presence of Missing Data
    (Public Library of Science, 2015) Liu, Shelley H.; Erion, Gabriel; Novitsky, Vladimir; Gruttola, Victor De
    Analyses of viral genetic linkage can provide insight into HIV transmission dynamics and the impact of prevention interventions. For example, such analyses have the potential to determine whether recently-infected individuals have acquired viruses circulating within or outside a given community. In addition, they have the potential to identify characteristics of chronically infected individuals that make their viruses likely to cluster with others circulating within a community. Such clustering can be related to the potential of such individuals to contribute to the spread of the virus, either directly through transmission to their partners or indirectly through further spread of HIV from those partners. Assessment of the extent to which individual (incident or prevalent) viruses are clustered within a community will be biased if only a subset of subjects are observed, especially if that subset is not representative of the entire HIV infected population. To address this concern, we develop a multiple imputation framework in which missing sequences are imputed based on a model for the diversification of viral genomes. The imputation method decreases the bias in clustering that arises from informative missingness. Data from a household survey conducted in a village in Botswana are used to illustrate these methods. We demonstrate that the multiple imputation approach reduces bias in the overall proportion of clustering due to the presence of missing observations.