Epidemiology & Biostatistics

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End date: 2019Subject: search.filters.subject.Biostatistics

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    COMPLIANCE WITH AGE AT INITIATION OF HUMAN PAPILLOMAVIRUS VACCINE SERIES BY SOCIOECONOMIC STATUS, RACE/ETHNICITY, AND HEALTH INSURANCE COVERAGE AMONG 13-17 YEAR-OLD FEMALES WHO RECEIVED AT LEAST ONE HPV VACCINE SHOT: UNITED STATES, 2011
    (2014) Rattanawatkul, Kanokphan; Carter-Pokras, Olivia; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Human Papillomavirus (HPV) vaccine has been shown to prevent cervical cancer. Numerous studies have examined factors associated with HPV vaccine series initiation, but little is known about factors associated with age of initiation of HPV vaccine. Using cross-sectional data from the 2011 National Immunization Survey-Teen, this study examined the relationship between Advisory Committee on Immunization Practices' recommended age at initiation of the HPV vaccine series and socioeconomic status, race/ethnicity, and health insurance among 13-17 year-old females who received at least one HPV vaccine shot (n=5,965). On-time initiation of HPV vaccine series was significantly associated with having public health insurance (AOR: 1.825, 95% CI: 1.266, 2.631). Females with college-graduated mothers (AOR: 0.669, 95% CI: 0.487, 0.918) or household income greater than $75,000 (AOR: 0.746, 95% CI: 0.568, 0.98) were less likely to initiate on-time. Research is needed to further investigate the reasons for late initiation among these subgroups.
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    Risk prediction models for hip fracture: parametric versus Cox regression
    (2013) Loo, Geok Yan; Ting Lee, Mei-ling; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Hip fracture is a public health burden due to high morbidity, mortality and cost. Risk prediction models can aid clinical decision-making by identifying individuals at risk. Objective: To build risk prediction model for incident hip fracture using Weibull regression and compare this with Cox regression model. Method: The Study of Osteoporosis prospectively collected risk factors were used to build a risk prediction model for first hip fracture using Threshold regression with Wiener process. Similar predictors were fitted using Cox regression for comparison. Results: There were 632 first hip fractures. Age, bone density, maternal and personal prior fractures were significant risk factors for hip fracture. Weibull had better goodness of fit, higher D-statistic and R-squared values than the exponential. Models did not differ in c-index and ten-fold cross validation showed similar areas under the ROC curves. Conclusion: Parametric and Cox models were comparable. External validation of the prediction model is required.
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    Selection of Fixed and Random Effects in Linear Mixed Effects Models With Applications to the Trial of Adolescent Girls
    (2013) Grant, Edward Michael; Wu, Tong Tong; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Linear mixed effect (LME) models have become popular in modeling data in a wide variety of fields, particularly in public health. These models are beneficial because they are able to account for both the means as well as the covariance structure of clustered or longitudinal data. However, as studies are able to collect an increasing amount of data for large numbers of predictors, a major challenge has been the selection of only important variables to create a more interpretable, parsimonious model. Previous methods for LME models have been inefficient in variable selection, but three new methods attempt to select and estimate both important fixed and important random effects simultaneously. The models are compared through analysis of simulated longitudinal data. Additionally, as an example of the important applications to public health, the methods are applied to the Trial of Activity in Adolescent Girls (TAAG) study, to determine important predictors for Moderate to Vigorous Physical Activity (MVPA).
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    DOUBLY PENALIZED LOGISTIC REGRESSION FOR GENOMEWIDE ASSOCIATION STUDIES WITH LINEARLY STRUCTURED GENETIC NETWORKS
    (2012) Li, Xia; Wu, Tongtong; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This research aims to integrate linear structures of genetic networks into genomewide analysis studies (GWAS). Lasso penalized logistic regression is ideally suited for continuous model selection in case-control disease gene mapping, especially when the number of predictor variables far exceeds the number of observations. But it fails to consider the structure of genetic networks. Imposing an additional weighted fused lasso can further remove irrelevant predictors. Nesterov's method is employed to handle the high dimensionality and complexity of genetic data. It also resolves the non-differentiability problem of the lasso and fused lasso penalties. In simulation studies, this proposed method shows advantages in some cases compared with lasso and fused lasso. We apply this method to the coeliac data on chromosome 8.
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    SEMIPARAMETRIC AND NONPARAMETRIC ANALYSIS FOR LONGITUDINAL DATA ON THE RELATIONSHIP BETWEEN CHILDHOOD EXTERNALIZING BEHAVIOR AND BODY MASS INDEX
    (2011) Wang, Kejia; He, Xin; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This thesis is an extension of the longitudinal data analysis of the association between externalizing behavior in early childhood and body mass index (BMI) from age 2 to 12 years conducted in Anderson et al. (2010). Externalizing behaviors problems are characterized by aggressive, oppositional, disruptive, or inattentive behaviors beyond those that would be expected given a child's age and development. The aim of the thesis is to estimate the children's BMI trajectory and to evaluate to what extent the externalizing behavior is related to BMI using semiparametric and nonparametric time-varying coefficient models. Some valuable insights into how the externalizing behavior and BMI are associated will be provided.