Browsing by Subject "Longitudinal Data Analysis"

Now showing 1 - 4 of 4
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    Advancement of Inferences for Genetic/Treatment Effects under Semiparametric Models
    (2025-05) Cui, Yishan; Wang, Honglang; Li, Fang; Peng, Hanxiang; Sarkar, Jyotirmoy
    This dissertation presents three methodological advancements in semiparametric modeling and inference, with applications in longitudinal data analysis and individualized treatment rules (ITRs). First, we enhance the semiparametric profile estimator for analyzing longitudinal data, addressing the challenge of within-subject correlation. By incorporating a nonparametric operator-regularized approach for estimating the covariance function, we develop a refined estimator that significantly improves efficiency over traditional local kernel smoothing methods, which assume an independent correlation structure. We further introduce an Empirical Likelihood (EL)-based inference method and demonstrate, through simulations and an application to the Genetic Analysis Workshop 18 dataset, that our approach attains the semiparametric efficiency bound and outperforms existing methods. Second, we propose a rank-based inference procedure for ITRs under a semiparametric single-index varying coefficient model, where the nonparametric coefficient function is assumed to be monotone increasing. By leveraging maximum rank correlation, our method circumvents direct estimation of the nonparametric function, thereby mitigating potential biases. For hypothesis testing, we derive the asymptotic distribution of the proposed estimator using de-biasing techniques. Monte Carlo simulations and an application to the ACTG175 dataset confirm the effectiveness of our approach. Finally, we develop a jackknife empirical likelihood ratio test to enhance hypothesis testing in the semiparametric single-index varying coefficient model. Existing methods often rely on plug-in variance-covariance estimators that approximate indicator functions using a sigmoid transformation, which are computationally complex and difficult to implement. Our proposed test offers a much simpler computational approach while achieving the same effectiveness. Extensive simulations and real-data analysis using the ACTG175 dataset further demonstrate the efficiency and practicality of our method. Together, these contributions enhance the efficiency and reliability of semiparametric estimation and inference, particularly in the contexts of longitudinal data analysis and individualized treatment decision-making.
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    Analyzing Participant Feedback on various training components to enhance future HANDS Intensive trainings (2006-2025)
    (2025-05-09) Maddipatla, Vignitha; Neal, Tiffany; Gottipati, Mounika; Swiezy, Naomi
    This project analyzed nearly two decades of participant feedback from HANDS in Autism® Intensive Trainings conducted between 2006 and 2025. The goal was to identify satisfaction trends and improvement opportunities in training logistics, content, communication, and participant engagement. Using REDCap datasets, the data was cleaned, standardized, and analyzed using Python, Power BI, and Excel. Results revealed consistently high satisfaction scores (averaging 4.8/5), with increased engagement over the course of each training week. Top-rated components included speaker knowledge and small group activities, while lecture engagement showed room for improvement. The project demonstrated the value of health informatics in translating large-scale feedback into actionable insights and highlighted the importance of data-driven strategies to enhance the delivery of autism-focused professional training programs.
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    A Longitudinal Analysis to Compare a Tailored Web-Based Intervention and Tailored Phone Counseling to Usual Care for Improving Beliefs of Colorectal Cancer Screening
    (2018-07) Dorman, Hannah Louise; Monahan, Patrick; Stump, Timothy; Bakoyannis, Giorgos; Lourens, Spencer
    An analysis of longitudinal data collected about beliefs regarding colorectal cancer (CRC) screenings at three-time points was analyzed to determine whether the beliefs improved from either the Web-Based, Phone-Based, or Web + Phone interventions compared to Usual Care. A mixed linear model adjusting for baseline and controlling for covariates was used to determine the effects of the intervention; Web-Based intervention was the most efficacious in improving beliefs, and phone intervention was also efficacious for several beliefs, compared to usual care.
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    Tracking Caregiver Strain Across Intervention Stages: A Data-Driven Approach to Emotional and Logistical Burden in Autism Support
    (2025-05-09) Vontimitta, Mahitha; Neal , Tiffany; Devarapalli, Baby Amulya; Swiezy, Naomi
    This project analyzed caregiver burden using longitudinal data from the Caregiver Strain Questionnaire (CSQ), collected across seven post-discharge timepoints as part of the Coordinated Care Team at HANDS in Autism®. Data from REDCap was cleaned and preprocessed using Python, then visualized with Power BI to uncover trends in objective, internalized, and externalized strain. Results showed peak caregiver burden during the preadmission and early post-discharge phases, with gradual improvement over time. However, emotional strain persisted longer than practical burdens. Visual dashboards allowed for real-time comparison of severity types and helped identify intervention windows. The project emphasized the value of data-driven tools in understanding healthcare challenges and reinforced the need for early support strategies, long-term emotional care, and improved caregiver retention in follow-up studies.