PhD, University of Cincinnati, 2022, Medicine: Biostatistics (Environmental Health)

Missing data are a problem in many scientific studies and have been addressed by many statisticians in the past two decades. Especially, exposure assessment in epidemiological research could be difficult with low concentrations in biologic samples. This results in a left-truncated missingness due to the limit of detection, which applies to both cross-sectional and longitudinal studies. Recently, an increasing number of literatures have emphasized the importance of including analysis model information in imputation, which is referred to as model-based imputation. For example, in epidemiological study, the analysis model information can be interaction terms between exposures and time or gender. The author proposed a model-based multiple imputation algorithm by chained-equations for multilevel data subject to limit of detection. This method accommodates (1) the detection limits, (2) multilevel data structure, as well as (3) complicated analysis model information, which can be used for both multilevel regression models with level-1 outcome and multiple informant models with level-2 outcome. To the best of the author's knowledge, there is no existing literature that has simultaneously addressed all three aspects in one algorithm. Simulation studies showed that the proposed algorithm outplayed traditional imputation methods with multilevel regression models and remained competitive with multiple informant models. The author further applied this method to the HOME Study data imputation. HOME Study collects longitudinal environmental chemical exposures and investigates their impact on a cohort of pregnant women in Cincinnati, Ohio. Specifically, the author would like to study the association of urinary OPEs concentration and children's reading ability. The result shows that OPE concentrations are inversely associated with the Reading Composite score at 8 years without covariate adjustment.

Committee: Changchun Xie Ph.D. (Committee Member); Aimin Chen Ph.D. (Committee Member); Nanhua Zhang Ph.D. (Committee Member); Marepalli Rao Ph.D. (Committee Member); Todd Jenkins Ph.D. (Committee Member) Subjects: Biostatistics

Master of Arts, Case Western Reserve University, 0, Psychology

At some point in their lives, everyone will experience a stressful life event. Usually, someone can cope with and make meaning from the event. However, the body of research investigating the impact of severe and/or chronic exposure to stressful life events on the brain shows that harmful effects of stress exposure accumulate. Considering the extant literature regarding religious meaning making in light of these findings and the robust literature on spiritual transformation following stressful life events, I developed three hypotheses: 1) stressful life events increase risk of (non)religious ID change, 2) earlier events continued to impact later ID changes, and 3) risk of ID change was similar across change groups. This study analyzed a nationally representative longitudinal dataset of US children born between 1980 and 1984 (N = 8984). The final analyses used multiple imputation to account for missing data and did not find evidence supporting the hypotheses.

Committee: Julie Exline Ph.D. (Committee Chair); Heath Demaree Ph.D. (Committee Member); Arin Connell Ph.D. (Committee Member) Subjects: Health; Mental Health; Psychology; Religion; Spirituality