School of Public Health
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12503/21845
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Browsing School of Public Health by Author "Aryal, Subhash"
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Item A treatise on independent component analysis in the presence of noise -- simulation and data applications in neuroimaging(2019-08) Mamun, Md Abdullah; Nandy, Rajesh R.; Aryal, Subhash; Mun, Eun-YoungThis doctoral thesis introduces a novel negentropy-based algorithm for model order selection in the presence of stationary colored Gaussian noise in the context of independent component analysis (ICA). Model order selection is a critical step in ICA because overestimation of model order selection may lead to splitting original source components, whereas underestimation may lead to loss of information. The existing order selection methods are prone to overestimation in the presence of colored Gaussian noise. The proposed negentropy-based order selection algorithm is aimed at overcoming this problem. This thesis provides a technical description of the method and reports the results from simulation experiments across a range of data conditions as well as real data applications. The new ICA estimation algorithm, noisyICA, extends the application of Hyvärinen's "fast fixed-point algorithm" for high dimensional data in the presence of white or stationary colored Gaussian noise. The first step of the algorithm is to reduce the dimension of observed data based on the proposed negentropy-based model order selection method. The next step of the noisyICA algorithm is to quasi-whiten data utilizing the noise covariance matrix, replacing the standard whitening procedure. Finally, the algorithm optimizes a contrast function based on Gaussian moments that removes biases due to Gaussian noise. Based on the simulation experiments, noisyICA performed well in comparison with fastICA in terms of bias reduction when estimating an ICA mixing matrix, and provided a reasonable and valid estimation of the ICA mixing matrix. The utility and feasibility of using the negentropy-based algorithm in model order selection is demonstrated in an analysis of two independent fMRI data sets. The first data set came from a task-related fMRI study that observed prescription opiate-dependent patients and healthy control subjects at resting state. The analysis of between-subjects and within-subject conditions demonstrated that the negentropy-based algorithm is consistent and robust to changes in data dimension. The second data analysis utilized the resting-state fMRI data from 25 patients with autism spectrum disorders (ASD). The performance of the negentropy-based algorithm was comparable to other commonly used methods. Finally, resting-state fMRI data from 10 ASD patients and 10 healthy control subjects were compared for brain region activation using group ICA. Brain activation (vision, default mode, and basal ganglia) was better represented for the healthy control subjects than the ASD patients. In sum, noisyICA as an alternative to the existing ICA estimation algorithms is promising based on the simulation analyses. In comparison with fastICA algorithm, noisyICA reduces bias in estimating the mixing matrix of independent components for high dimensional data that contain Gaussian noise.Item All-cause and CVD-specific mortality associated with allergic rhinitis and allergic asthma: a retrospective cohort study(2014-08-01) Gandhi, Subi; Felini, Martha; El-Faramawi, Mohammed; Aryal, SubhashAsthma has allergic and non-allergic forms. Asthma has been associated with all-cause mortality and CVD-specific mortality, but the role of allergic asthma on these outcomes is unknown. Additionally, allergic rhinitis and its concomitant role with allergic asthma have not been explored for these two specific outcomes. The present study utilized NHANES III and NHANES III-linked mortality files to study the risk of all-cause and CVD-specific mortality due to allergic rhinitis and allergic asthma, independently and concurrently. Unlike previously conducted studies, this is the first cohort study that uniquely focused on allergic forms of rhinitis and asthma using a representative sample of the general US population. Men and women of all racial/ethnic backgrounds (≥40 years) whose mortality was ascertained via National Death Index were included in this study. Hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were calculated for those with self-reported allergic conditions only, as well as for those that self-reported the allergic conditions and underwent skin testing, for all-cause and CVD-specific mortality. The combined effect for all-cause mortality {HR: 1.23 (95%CI: 0.91, 1.29)} and CVD-specific mortality {HR: 1.66 (95%CI: 0.85, 3.25)} was observed among those that self-reported allergic rhinitis and allergic asthma concurrently. However, results were null among skin test completers only. Based on our findings health practitioners should assess both the conditions simultaneously for better management of these symptoms to reduce the burden of all-cause and CVD-specific mortality.Item Changes in Healthcare Utilization and Charges Among Supportive Housing Residents Enrolled in a Health Coaching Program(2019-05) Chhetri, Shlesma; Spence-Almaguer, Emily; Walters, Scott T.; Stockbridge, Erica L.; Aryal, SubhashThe effectiveness of self-management programs on healthcare use outcomes is an active area of research with inconsistent results. This study was the first to evaluate changes in healthcare utilization (including hospital encounters, inpatient visits, outpatient visits, and emergency visits) and charged amounts among supportive housing residents enrolled in a health coaching program. We utilized retrospective longitudinal medical claims data and a qualitative examination of participants' perceptions of the program's influence on their healthcare use. Zero-inflated negative binomial model and log-gamma models were used to assess change in count variables and charged amounts respectively. Although participants reported a positive impact of the program on their overall quality of life through improved health self-management strategies, the analysis of claims data showed no significant change in healthcare use and charged amounts in all analyses spanning 12 months prior to 24 months post enrollment. These findings may potentially demonstrate the success of health coaching programs in stabilizing healthcare utilization among individuals who otherwise might have increased their healthcare use over time. During interviews and focus groups, participants also shared personal and systems level challenges that influenced their healthcare use. The inclusion of a control group in future analyses would help measure the actual impact of health coaching on healthcare utilization measures among supportive housing residents with high health needs.Item Improving the Efficiency of A and D Optimal Designs for Dose Response Models(2021-08) Jasti, Srichand; Nandy, Rajesh R.; Aryal, Subhash; Thombs, Dennis; Barnett, Tracey; Haque, UbydulFor A-optimality, by virtue of Cramér–Rao bound, the trace of the inverse of Information matrix for the parameters serves as a lower bound for the sum of variances of the estimators and the bound is attained asymptotically. Hence, asymptotically, A-optimality is achieved by minimizing the trace of the inverse of the Information matrix. For non-linear models, Cramér–Rao bound is crude for finite samples and hence the asymptotic solution can be very different from the design that minimizes the sum of variances. We explore the validity of the asymptotic solution by directly minimizing the sum of variances using numerical methods in a restricted search space. We demonstrate that even in a very restrictive search space of point symmetric designs, the theoretical solution is half as efficient for a sample size of 100. Further improvement can be achieved by relaxing the restriction of the solution being point symmetric. The solution to A and D optimal designs for the logistic model depend on the unknown parameters of the model. Therefore, to obtain an optimal design the experimenter must inform the design based on some prior knowledge, or a guess, of the unknown parameters. This is a severe limitation on the ability to identify an optimal design especially when there is little prior information to inform the guess. Here we explore the use of a two-stage A-optimal design for finite samples and three-stage D-optimal design for large samples to mitigate the loss in efficiency which may arise due to poor guess values. We demonstrate that while two-stage finite sample model results in gain in efficiency with small sample sizes at 70% allocation to the first stage. The three-stage D optimal design is shown to be almost always better than the single stage and the corresponding two-stage design.