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Browsing by Author "Sharp, Julia L., advisor"

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    Family caregiving, family dynamics, and preparedness for the transition to end-of-life care
    (Colorado State University. Libraries, 2021) Fox, Aimee Lynn, author; Fruhauf, Christine A., advisor; Sharp, Julia L., advisor; Diehl, Manfred, committee member; Luong, Gloria, committee member; Atler, Karen E., committee member
    Taking on the role of family caregiver to an adult family member with health or functional needs can be a time consuming, stressful, and physically demanding responsibility, and often leads to adverse psychological or physical outcomes. As family members near the end of their life, their physical, emotional, social, and spiritual care needs may become increasingly complex, and family caregivers are an integral part of providing care and comfort during this time. Yet, individuals providing end-of-life (EOL) care for a family member are vulnerable to additional emotional and psychological stress and strain, and often indicate they do not have the knowledge or skills needed for providing this type of care. Little is known about what factors may help family caregivers feel more prepared for EOL caregiving, or how family dynamics (such as relationships, interactions, and communication) between the caregiver, care receiver, and other family members may affect these feelings of preparedness. Thus, the purpose of this dissertation was to explore how family caregivers perceive their preparedness for the transition to EOL caregiving and how family dynamics may be associated with feelings of preparedness. To frame this work, the manuscript in Chapter 2 presents the Conceptual Framework for a Bioecological Model of Family Dynamics and the Transition to EOL Caregiving. This model is an innovative theoretical approach to investigating the various individual- and family-level contexts that may affect family caregiver outcomes. The conceptual framework provides a tool to examine family caregivers' personal characteristics, family contexts (such as the familial relation between the caregiver and care receiver), factors of time (such as duration of care and hours of care provided each week), and family processes (such as advance care planning conversations) that may be connected to perceived preparedness for the transition to EOL caregiving. The study presented in Chapter 3 utilizes the conceptual framework to explore family caregivers' perceived preparedness for caregiving. Results indicate that overall, family caregivers feel somewhat prepared to provide care to their care receiver but feel not too well prepared for the transition to EOL caregiving, regardless of age, gender, race, ethnicity, or education. The study presented in Chapter 4 builds on these findings and explores how family dynamics may be associated with family caregivers' feelings of preparedness. The results of this study failed to demonstrate an association between the constructs of family dynamics and caregiver preparedness, and several theoretical and methodological considerations are examined to potentially explain these findings. It may be that family dynamics are not well understood in caregiving families, and different elements of family dynamics are important at different stages of caregiving and during the transition to EOL care. The results, strengths, and limitations of this comprehensive dissertation study should inform future basic and applied studies to advance family caregiving research. Importantly, there is a need to development more valid and reliable measures of family dynamics for aging and caregiving families, and interventions to help families prepare for future care needs and caregiving transitions such as the transition to EOL care. As researchers and practitioners learn more about how to prepare family caregivers and their families for the transition to EOL care, this may improve family caregiver and family-level outcomes, and help families best meet the care wishes and improve life satisfaction for individuals at the end of their life.
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    Functional methods in outlier detection and concurrent regression
    (Colorado State University. Libraries, 2024) Creutzinger, Michael L., author; Cooley, Daniel, advisor; Sharp, Julia L., advisor; Koslovsky, Matt, committee member; Liebl, Dominik, committee member; Ortega, Francisco, committee member
    Functional data are data collected on a curve, or surface, over a continuum. The growing presence of high-resolution data has greatly increased the popularity of using and developing methods in functional data analysis (FDA). Functional data may be defined differently from other data structures, but similar ideas apply for these types of data including data exploration, modeling and inference, and post-hoc analyses. The methods presented in this dissertation provide a statistical framework that allows a researcher to carry out an analysis of functional data from "start to finish''. Even with functional data, there is a need to identify outliers prior to conducting statistical analysis procedures. Existing functional data outlier detection methodology requires the use of a functional data depth measure, functional principal components, and/or an outlyingness measure like Stahel-Donoho. Although effective, these functional outlier detection methods may not be easily interpreted. In this dissertation, we propose two new functional outlier detection methods. The first method, Practical Outlier Detection (POD), makes use of ordinary summary statistics (e.g., minimum, maximum, mean, variance, etc). In the second method, we developed a Prediction Band Outlier Detection (PBOD) method that makes use of parametric, simultaneous, prediction bands that meet nominal coverage levels. The two new outlier detection methods were compared to three existing outlier detection methods: MS-Plot, Massive Unsupervised Outlier Detection, and Total Variation Depth. In the simulation results, POD performs as well, or better, than its counterparts in terms of specificity, sensitivity, accuracy, and precision. Similar results were found for PBOD, except for noticeably smaller values of specificity and accuracy than all other methods. Following data exploration and outlier detection, researchers often model their data. In FDA, functional linear regression uses a functional response Yi(t) and scalar and/or functional predictors, Xi(t). A functional concurrent regression model is estimated by regressing Yi on Xi pointwise at each sampling point, t. After estimating a regression model (functional or non-functional), it is common to estimate confidence and prediction intervals for parameter(s), including the conditional mean. A common way to obtain confidence/prediction intervals for simultaneous inference across the sampling domain is to use resampling methods (e.g., bootstrapping or permutation). We propose a new method for estimating parametric, simultaneous confidence and prediction bands for a functional concurrent regression model, without the use of resampling. The method uses Kac-Rice formulas for estimation of a critical value function, which is used with a functional pivot to acquire the simultaneous band. In the results, the proposed method meets nominal coverage levels for both confidence and prediction bands. The method we propose is also substantially faster to compute than methods that require resampling techniques. In linear regression, researchers may also assess if there are influential observations that may impact the estimates and results from the fitted model. Studentized difference in fits (DFFITS), studentized difference in regression coefficient estimates (DFBETAS), and/or Cook's Distance (D) can all be used to identify influential observations. For functional concurrent regression, these measures can be easily computed pointwise for each observation. However, the only current development is to use resampling techniques for estimating a null distribution of the average of each measure. Rather than using the average values and bootstrapping, we propose working with functional DFFITS (DFFITS(t)) directly. We show that if the functional errors are assumed to follow a Gaussian process, DFFITS(t) is distributed uniformly as a scaled Student's t process. Then, we propose using a multivariate Student's t distributional quantile for identifying influential functional observations with DFFITS(t). Our methodology ("Theoretical'') is compared against a competing method that uses a parametric bootstrapping technique ("Bootstrapped'') for estimating the null distribution of the mean absolute value of DFFITS(t). In the simulation and case study results, we find that the Theoretical method greatly reduces the computation time, without much loss in performance as measured by accuracy (ACC), precision (PPV), and Matthew's Correlation Coefficient (MCC), than the Bootstrapped method. Furthermore, the average sensitivity of the Theoretical method is higher in all scenarios than the Bootstrapped method.