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  • 1
    Description: Multiple-outcome longitudinal data are abundant in clinical investigations. For example, infections with different pathogenic organisms are often tested concurrently, and assessments are usually taken repeatedly over time. It is therefore natural to consider a multivariate modeling approach to accommodate the underlying interrelationship among the multiple longitudinally measured outcomes. This dissertation proposes a multivariate semiparametric modeling framework for such data. Relevant estimation and inference procedures as well as model selection tools are discussed within this modeling framework. The first part of this research focuses on the analytical issues concerning binary data. The second part extends the binary model to a more general situation for data from the exponential family of distributions. The proposed model accounts for the correlations across the outcomes as well as the temporal dependency among the repeated measures of each outcome within an individual. An important feature of the proposed model is the addition of a bivariate smooth function for the depiction of concurrent nonlinear and possibly interacting influences of two independent variables on each outcome. For model implementation, a general approach for parameter estimation is developed by using the maximum penalized likelihood method. For statistical inference, a likelihood-based resampling procedure is proposed to compare the bivariate nonlinear effect surfaces across the outcomes. The final part of the dissertation presents a variable selection tool to facilitate model development in practical data analysis. Using the adaptive least absolute shrinkage and selection operator (LASSO) penalty, the variable selection tool simultaneously identifies important fixed effects and random effects, determines the correlation structure of the outcomes, and selects the interaction effects in the bivariate smooth functions. Model selection and estimation are performed through a two-stage procedure based on an expectation-maximization (EM) algorithm. Simulation studies are conducted to evaluate the performance of the proposed methods. The utility of the methods is demonstrated through several clinical applications.
    Keywords: Biostatistics ; Regression Analysis -- Data Processing -- Research -- Methodology ; Mathematical Statistics -- Longitudinal Studies -- Research ; Multivariate Analysis -- Research -- Methodology ; Estimation Theory -- Research ; Biometry -- Methodology -- Research ; Clinical Trials -- Statistical Methods -- Research ; Expectation-Maximization Algorithms -- Research ; Binary System (Mathematics) -- Research ; Nonparametric Statistics -- Research ; Probabilities -- Data Processing ; Real-Time Data Processing -- Research ; Parameter Estimation -- Research ; Latent Variables -- Research ; Meta-Analysis -- Research -- Methodology ; Stochastic Processes -- Research ; Least Squares -- Research
    Source: Networked Digital Library of Theses and Dissertations
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  • 2
    Description: Indiana University-Purdue University Indianapolis (IUPUI) Background: High blood pressure (HBP) is a common risk factor for dementia in elder population. Anti-hypertensive medications have been reported to associate with lower incidence rate of dementia in elder African Americans. The Apolipoprotein E (ApoE) epsilon 4 allele has been shown to be associated with both increased dementia and hypertension risk. However, previous studies had not examined the association between anti-hypertensive medications by ApoE status accounting for the competing risk from death. Methods: This is a prospective observational cohort study in 1236 community-dwelling hypertensive African Americans aged 65 years and older without dementia at baseline, with follow-up cognitive assessment and clinical evaluation for dementia diagnosis. Dementia-free mortality was considered as the competing risk. Of these, 707 participants were genotyped for ApoE status. Anti-hypertensive medication use was obtained from prescription records in the electronic medical records of the Indiana Network for Patient Care (INPC). Cox proportional cause-specific hazard (CSH) regression models were applied to assess the association between anti-hypertensive medication use and CSHs for dementia and death in ApoE epsilon 4 carriers and non-carriers separately. Key results: In ApoE epsilon 4 carriers, participants using anti-hypertensive medications had lower CSH of dementia compared to those not on anti-hypertensive medications before adjusting for blood pressure (BP) (hazard ratio (HR), 0.365; 95% CI, 0.170 – 0.785; p = 0.0099). The HR was no longer significant once BP control was adjusted (HR, 0.784; 95% CI, 0.197 – 3.123; p = 0.7303). Anti-hypertensive medications were not associated with dementia rate in non-carriers. In ApoE epsilon 4 non-carriers, participants on anti-hypertensive treatment showed significantly lower CSH of death compared to those not on mediations adjusting for covariates and BP control (HR, 0.237; 95% CI, 0.149 – 0.375; p 〈 0.0001). There was no significant association between anti-hypertensive medication use and death in ApoE epsilon 4 carriers. Conclusions: Anti-hypertensive medication was associated with lower dementia rate in ApoE epsilon 4 carriers and that rate was primarily mediated through BP control. In non-carriers, anti-hypertensive medication was significantly associated with lower mortality rate and this association appears to be independent of BP control.
    Keywords: Survival Analysis ; Competing Risk ; Dementia ; Death
    Source: Networked Digital Library of Theses and Dissertations
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  • 3
    Description: Indiana University-Purdue University Indianapolis (IUPUI) Dementia studies often collect multiple longitudinal neuropsychological measures in order to examine patients' decline across a number of cognitive domains. Dementia patients have shown considerable heterogeneities in individual trajectories of cognitive decline, with some patients showing rapid decline following diagnoses while others exhibiting slower decline or remain stable for several years. In the first part of this dissertation, a multivariate finite mixture latent trajectory model was proposed to identify longitudinal patterns of cognitive decline in multiple cognitive domains with multiple tests within each domain. The expectation-maximization (EM) algorithm was implemented for parameter estimation and posterior probabilities were estimated based on the model to predict latent class membership. Simulation studies demonstrated satisfactory performance of the proposed approach. In the second part, a simulation study was performed to compare the performance of information-based criteria on the selection of the number of latent classes. Commonly used model selection criteria including the Akaike information criterion (AIC), Bayesian information criterion (BIC), as well as consistent AIC (CAIC), sample adjusted BIC (SABIC) and the integrated classification likelihood criteria (ICLBIC) were included in the comparison. SABIC performed uniformly better in all simulation scenarios and hence was the preferred criterion for our proposed model. In the third part of the dissertation, the multivariate finite mixture latent trajectory model was extended to situations where the true latent class membership was known for a subset of patients. The proposed models were used to analyze data from the Uniform Data Set (UDS) collected from Alzheimer's Disease Centers across the country to identify various cognitive decline patterns among patients with dementia.
    Keywords: Dementia ; Cognition In Old Age ; Cognition Disorders In Old Age -- Prevention ; Dementia -- Research ; Biology -- Statistics ; Computer Science ; Data Mining
    Source: Networked Digital Library of Theses and Dissertations
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  • 4
    Language: English
    Description: Thesis (Ph. D.)--University of California, Santa Cruz. Typescript. Includes bibliographical references.
    Keywords: Marcuse ; Herbert ; Philosophers.
    Source: Networked Digital Library of Theses and Dissertations
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  • 5
    Description: Indiana University-Purdue University Indianapolis (IUPUI) Survival analysis often encounters the situations of correlated multiple events including the same type of event observed from siblings or multiple events experienced by the same individual. In this dissertation, we focus on the joint modeling of bivariate time to event data with the estimation of the association parameters and also in the situation of a semi-competing risk. This dissertation contains three related topics on bivariate time to event mod els. The first topic is on estimating the cross ratio which is an association parameter between bivariate survival functions. One advantage of using cross-ratio as a depen dence measure is that it has an attractive hazard ratio interpretation by comparing two groups of interest. We compare the parametric, a two-stage semiparametric and a nonparametric approaches in simulation studies to evaluate the estimation perfor mance among the three estimation approaches. The second part is on semiparametric models of univariate time to event with a semi-competing risk. The third part is on semiparametric models of bivariate time to event with semi-competing risks. A frailty-based model framework was used to accommodate potential correlations among the multiple event times. We propose two estimation approaches. The first approach is a two stage semiparametric method where cumulative baseline hazards were estimated by nonparametric methods first and used in the likelihood function. The second approach is a penalized partial likelihood approach. Simulation studies were conducted to compare the estimation accuracy between the proposed approaches. Data from an elderly cohort were used to examine factors associated with times to multiple diseases and considering death as a semi-competing risk.
    Keywords: Copula ; Cross Ratio ; Frailty Model ; Multivariate ; Survival Analysis
    Source: Networked Digital Library of Theses and Dissertations
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  • 6
    Description: Indiana University-Purdue University Indianapolis (IUPUI) Objectives: The aims of this study are to estimate the cumulative incidence of lower extremity amputation (LEA) revision and reamputation adjusting for a competing risk of death, estimate the one-year event-free mortality rates for patients with peripheral vascular disease undergoing LEA, and develop predictive models for LEA revision and reamputation adjusting for a competing risk of death. Methods: This was a retrospective review of the prospectively collected Vascular Quality Initiative (VQI) registry between 2013 and 2018. Adults undergoing unilateral LEA were included. Demographics, comorbidities, medications, smoking status, history of vascular procedures and revascularization attempts, and procedure urgency were considered. Models to predict LEA revision and reamputation were developed using multivariable regression on the interval-censored competing risks data using semiparametric regression on the cumulative incidence function. Results: The cumulative incidences of LEA revision and revision-free mortality within one year of index amputation are 14.9% and 15.5% respectively. Patient BMI, smoking status, aspirin use, history of revascularization, and level of planned LEA are significantly associated with the odds of LEA revision. Age, amputation urgency, dialysis, and level of planned LEA are associated with the one-year odds of revision-free mortality. A patient receiving an index above knee amputation (AKA) has 61% lower odds of LEA revision (p 〈 0.0001) but 51% higher odds of revision-free mortality following LEA (p 〈 0.0001). Previous revascularization procedures increase the odds of revision by 23% (p 〈 0.0001). The cumulative incidences of reamputation and one-year reamputation-free mortality following LEA are 11.5% and 16.9% respectively. Urgency of the procedure, history of revascularization procedures, and level of planned LEA are statistically associated with the odds of reamputation when adjusting for the competing risk of death. Patients receiving index AKA have 62% lower odds of reamputation (p 〈 0.0001) compared to BKA. Dialysis is the strongest predictor of one-year mortality (OR 2.576, p 〈 0.0001). Conclusions: Patients with appropriately managed PVD, which still progresses to amputation have higher odds of LEA revision and reamputation. Revision risk can be predicted and compared on the basis of patient factors and the planned index amputation.
    Keywords: Amputation ; Competing Risk ; Peripheral Vascular Disease
    Source: Networked Digital Library of Theses and Dissertations
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