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Articles 1 - 19 of 19
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An Adaptive Bayesian Approach To Dose-Response Modeling, Thomas J. Leininger
An Adaptive Bayesian Approach To Dose-Response Modeling, Thomas J. Leininger
Theses and Dissertations
Clinical drug trials are costly and time-consuming. Bayesian methods alleviate the inefficiencies in the testing process while providing user-friendly probabilistic inference and predictions from the sampled posterior distributions, saving resources, time, and money. We propose a dynamic linear model to estimate the mean response at each dose level, borrowing strength across dose levels. Our model permits nonmonotonicity of the dose-response relationship, facilitating precise modeling of a wider array of dose-response relationships (including the possibility of toxicity). In addition, we incorporate an adaptive approach to the design of the clinical trial, which allows for interim decisions and assignment to doses based …
Nonlinear Models In Multivariate Population Bioequivalence Testing, Bassam Dahman
Nonlinear Models In Multivariate Population Bioequivalence Testing, Bassam Dahman
Theses and Dissertations
In this dissertation a methodology is proposed for simultaneously evaluating the population bioequivalence (PBE) of a generic drug to a pre-licensed drug, or the bioequivalence of two formulations of a drug using multiple correlated pharmacokinetic metrics. The univariate criterion that is accepted by the food and drug administration (FDA) for testing population bioequivalence is generalized. Very few approaches for testing multivariate extensions of PBE have appeared in the literature. One method uses the trace of the covariance matrix as a measure of total variability, and another uses a pooled variance instead of the reference variance. The former ignores the correlation …
Parameter Estimation For The Lognormal Distribution, Brenda Faith Ginos
Parameter Estimation For The Lognormal Distribution, Brenda Faith Ginos
Theses and Dissertations
The lognormal distribution is useful in modeling continuous random variables which are greater than or equal to zero. Example scenarios in which the lognormal distribution is used include, among many others: in medicine, latent periods of infectious diseases; in environmental science, the distribution of particles, chemicals, and organisms in the environment; in linguistics, the number of letters per word and the number of words per sentence; and in economics, age of marriage, farm size, and income. The lognormal distribution is also useful in modeling data which would be considered normally distributed except for the fact that it may be more …
Joint Mixed-Effects Models For Longitudinal Data Analysis: An Application For The Metabolic Syndrome, John Thorp Iii
Joint Mixed-Effects Models For Longitudinal Data Analysis: An Application For The Metabolic Syndrome, John Thorp Iii
Theses and Dissertations
Mixed-effects models are commonly used to model longitudinal data as they can appropriately account for within and between subject sources of variability. Univariate mixed effect modeling strategies are well developed for a single outcome (response) variable that may be continuous (e.g. Gaussian) or categorical (e.g. binary, Poisson) in nature. Only recently have extensions been discussed for jointly modeling multiple outcome variables measures longitudinally. Many diseases processes are a function of several factors that are correlated. For example, the metabolic syndrome, a constellation of cardiovascular risk factors associated with an increased risk of cardiovascular disease and type 2 diabetes, is often …
Deriving Optimal Composite Scores: Relating Observational/Longitudinal Data With A Primary Endpoint, Rhonda Ellis
Deriving Optimal Composite Scores: Relating Observational/Longitudinal Data With A Primary Endpoint, Rhonda Ellis
Theses and Dissertations
In numerous clinical/experimental studies, multiple endpoints are measured on each subject. It is often not clear which of these endpoints should be designated as of primary importance. The desirability function approach is a way of combining multiple responses into a single unitless composite score. The response variables may include multiple types of data: binary, ordinal, count, interval data. Each response variable is transformed to a 0 to1 unitless scale with zero representing a completely undesirable response and one representing the ideal value. In desirability function methodology, weights on individual components can be incorporated to allow different levels of importance to …
A Sequential Algorithm To Identify The Mixing Endpoints In Liquids In Pharmaceutical Applications, Akriti Saxena
A Sequential Algorithm To Identify The Mixing Endpoints In Liquids In Pharmaceutical Applications, Akriti Saxena
Theses and Dissertations
The objective of this thesis is to develop a sequential algorithm to determine accurately and quickly, at which point in time a product is well mixed or reaches a steady state plateau, in terms of the Refractive Index (RI). An algorithm using sequential non-linear model fitting and prediction is proposed. A simulation study representing typical scenarios in a liquid manufacturing process in pharmaceutical industries was performed to evaluate the proposed algorithm. The data simulated included autocorrelated normal errors and used the Gompertz model. A set of 27 different combinations of the parameters of the Gompertz function were considered. The results …
Estimating The Effect Of Disability On Medicare Expenditures, David Morris Burk
Estimating The Effect Of Disability On Medicare Expenditures, David Morris Burk
Theses and Dissertations
We consider the effect of disability status on Medicare expenditures. Disabled elderly historically have accounted for a significant portion of Medicare expenditures. Recent demographic trends exhibit a decline in the size of this population, causing some observers to predict declines in Medicare expenditures. There are, however, reasons to be suspicious of this rosy forecast. To better understand the effect of disability on Medicare expenditures, we develop and estimate a model using the generalized method of moments technique. We find that newly disabled elderly generally spend more than those who have been disabled for longer periods of time. Also, we find …
Zero-Inflated Censored Regression Models: An Application With Episode Of Care Data, Jonathan P. Prasad
Zero-Inflated Censored Regression Models: An Application With Episode Of Care Data, Jonathan P. Prasad
Theses and Dissertations
The objective of this project is to fit a sequence of increasingly complex zero-inflated censored regression models to a known data set. It is quite common to find censored count data in statistical analyses of health-related data. Modeling such data while ignoring the censoring, zero-inflation, and overdispersion often results in biased parameter estimates. This project develops various regression models that can be used to predict a count response variable that is affected by various predictor variables. The regression parameters are estimated with Bayesian analysis using a Markov chain Monte Carlo (MCMC) algorithm. The tests for model adequacy are discussed and …
Meta-Analysis Using Bayesian Hierarchical Models In Organizational Behavior, Michael David Ulrich
Meta-Analysis Using Bayesian Hierarchical Models In Organizational Behavior, Michael David Ulrich
Theses and Dissertations
Meta-analysis is a tool used to combine the results from multiple studies into one comprehensive analysis. First developed in the 1970s, meta-analysis is a major statistical method in academic, medical, business, and industrial research. There are three traditional ways in which a meta-analysis is conducted: fixed or random effects, and using an empirical Bayesian approach. Derivations for conducting meta-analysis on correlations in the industrial psychology and organizational behavior (OB) discipline were reviewed by Hunter and Schmidt (2004). In this approach, Hunter and Schmidt propose an empirical Bayesian analysis where the results from previous studies are used as a prior. This …
Modeling Temperature Reduction In Tendons Using Gaussian Processes Within A Dynamic Linear Model, Richard David Wyss
Modeling Temperature Reduction In Tendons Using Gaussian Processes Within A Dynamic Linear Model, Richard David Wyss
Theses and Dissertations
The time it takes an athlete to recover from an injury can be highly influenced by training procedures as well as the medical care and physical therapy received. When an injury occurs to the muscles or tendons of an athlete, it is desirable to cool the muscles and tendons within the body to reduce inflammation, thereby reducing the recovery time. Consequently, finding a method of treatment that is effective in reducing tendon temperatures is beneficial to increasing the speed at which the athlete is able to recover. In this project, Bayesian inference with Gaussian processes will be used to model …
Comparing Bootstrap And Jackknife Variance Estimation Methods For Area Under The Roc Curve Using One-Stage Cluster Survey Data, Allison Dunning
Comparing Bootstrap And Jackknife Variance Estimation Methods For Area Under The Roc Curve Using One-Stage Cluster Survey Data, Allison Dunning
Theses and Dissertations
The purpose of this research is to examine the bootstrap and jackknife as methods for estimating the variance of the AUC from a study using a complex sampling design and to determine which characteristics of the sampling design effects this estimation. Data from a one-stage cluster sampling design of 10 clusters was examined. Factors included three true AUCs (.60, .75, and .90), three prevalence levels (50/50, 70/30, 90/10) (non-disease/disease), and finally three number of clusters sampled (2, 5, or 7). A simulated sample was constructed for each of the 27 combinations of AUC, prevalence and number of clusters. Estimates of …
Xprime: A Method Incorporating Expert Prior Information Into Motif Exploration, Rachel Lynn Poulsen
Xprime: A Method Incorporating Expert Prior Information Into Motif Exploration, Rachel Lynn Poulsen
Theses and Dissertations
One of the primary goals of active research in molecular biology is to better understand the process of transcription regulation. An important objective in understanding transcription is identifying transcription factors that directly regulate target genes. Identifying these transcription factors is a key step toward eliminating genetic diseases or disease susceptibilities that are encoded inside deoxyribonucleic acid (DNA). There is much uncertainty and variation associated with transcription factor binding sites, requiring these sites to be represented stochastically. Although typically each transcription factor prefers to bind to a specific DNA word, it can bind to different variations of that DNA word. In …
Detecting Near-Earth Objects Using Cross-Correlation With A Point Spread Function, Anthony P. O'Dell
Detecting Near-Earth Objects Using Cross-Correlation With A Point Spread Function, Anthony P. O'Dell
Theses and Dissertations
This thesis describes a process to help discover Near-Earth Objects (NEOs) of larger than 140 meters in diameter from ground based telescopes. The process involves using Nyquist sampling rate to take data from a ground-based telescope and measuring the atmospheric seeing parameter, r0, at the time of data collection. r0 is then used to create a point spread function (PSF) for a NEO at the visual magnitude limit of the telescope and exposure time. This PSF is cross-correlated with the Nyquist sampling rate image from the telescope to reduce the noise and therefore increase the detection probability of …
Measuring Skill Importance In Women's Soccer And Volleyball, Michelle L. Allan
Measuring Skill Importance In Women's Soccer And Volleyball, Michelle L. Allan
Theses and Dissertations
The purpose of this study is to demonstrate how to measure skill importance for two sports: soccer and volleyball. A division I women's soccer team filmed each home game during a competitive season. Every defensive, dribbling, first touch, and passing skill was rated and recorded for each team. It was noted whether each sequence of plays led to a successful shot. A hierarchical Bayesian logistic regression model is implemented to determine how the performance of the skill affects the probability of a successful shot. A division I women's volleyball team rated each skill (serve, pass, set, etc.) and recorded rally …
Creating Multi Objective Value Functions From Non-Independent Values, Christopher D. Richards
Creating Multi Objective Value Functions From Non-Independent Values, Christopher D. Richards
Theses and Dissertations
Decisions are made every day and by everyone. As these decisions become more important, involve higher costs and affect a broader group of stakeholders it becomes essential to establish a more rigorous strategy than simply intuition or "going with your gut". In the past several decades, the concept of Value Focused Thinking (VFT) has gained much acclaim in assisting Decision Makers (DMs) in this very effort. By identifying and organizing what a DM values VFT is able to decompose the original problem and create a mathematical model to score and rank alternatives to be chosen. But what if the decision …
Using Agent-Based Modeling To Evaluate Uas Behaviors In A Target-Rich Environment, Joseph A. Van Kuiken
Using Agent-Based Modeling To Evaluate Uas Behaviors In A Target-Rich Environment, Joseph A. Van Kuiken
Theses and Dissertations
The trade-off between accuracy and speed is a re-occurring dilemma in many facets of military performance evaluation. This is an especially important issue in the world of ISR. One of the most progressive areas of ISR capabilities has been the utilization of Unmanned Aircraft Systems (UAS). Many people believe that the future of UAS lies in smaller vehicles flying in swarms. We use the agent-based System Effectiveness and Analysis Simulation (SEAS) to create a simulation environment where different configurations of UAS vehicles can process targets and provide output that allows us to gain insight into the benefits and drawbacks of …
Robust Sensitivity Analysis For The Joint Improvised Explosive Device Defeat Organization (Jieddo) Proposal Selection Model, Christina J. Willy
Robust Sensitivity Analysis For The Joint Improvised Explosive Device Defeat Organization (Jieddo) Proposal Selection Model, Christina J. Willy
Theses and Dissertations
Throughout Operations Iraqi Freedom and Enduring Freedom, the Department of Defense (DoD) faced challenges not experienced in our previous military operations. The enemy’s unwavering dedication to the use of improvised explosive devices (IEDs) against the coalition forces continues to challenge the day-to-day operations of the current war. The Joint Improvised Explosive Device Defeat Organization’s (JIEDDO) proposal solicitation process enables military and non-military organizations to request funding for the development of Counter-Improvised Explosive Device (C-IED) projects. Decision Analysis (DA) methodology serves as a tool to assist the decision maker (DM) in making an informed decision. This research applies Value Focused Thinking …
Demonstration And Verification Of A Broad Spectrum Anomalous Dispersion Effects Tool For Index Of Refraction And Optical Turbulence Calculations, J. Jean Cohen
Theses and Dissertations
An atmospheric optical turbulence strength model with a broad wavelength range of 355nm (ultraviolet) to 8.6m (radio frequencies) has been created at AFIT and implemented into the High Energy Laser End-to-End Operational Simulation tool (HELEEOS). This modeling and simulation tool is a first principles atmospheric propagation and characterization model. Within HELEEOS lies the High-Resolution Transmission Molecular Absorption (HITRAN) database, containing 1,734,469 spectral lines for 37 different molecules as of version 12.0 (2004). HITRAN affords HELEEOS incredible accuracy for electromagnetic (EM) propagation prediction. A full understanding of optical turbulence is needed to successfully predict EM radiation propagation, particularly within the application …
Characterizing The Statistical Properties And Global Distribution Of Dansgaard-Oeschger Events, Andrea Michelle Thomas
Characterizing The Statistical Properties And Global Distribution Of Dansgaard-Oeschger Events, Andrea Michelle Thomas
Theses and Dissertations
Ice core records from Greenland have shown times of rapid warming during the most recent glacial period, called Dansgaard-Oeschger (D-O) events. D-O events are important to our understanding of both past climate systems and modern climate volatility. In this paper, we present new approaches for statistically evaluating the existence of cyclicity in D-O events and the possible lagged correlation between the Greenland and Antarctica temperature records. Specifically, we consider permutation testing and bootstrapping methodologies for assessing the cyclicity of D-O events and the correlation between the Greenland and Antarctica records. We find that there is not enough evidence to conclude …