Statistical Methodology Commons^™

Modeling The Bidirectional Relationship Between Shared-Patient Physician Networks And Patient Longitudinal Treatment Patterns: Application To Physician Risky-Prescribing, Xin Ran

Dartmouth College Ph.D Dissertations

Risky-prescribing is a pressing public health concern in the United States. Opioids, benzodiazepines, and non-benzodiazepine sedative-hypnotics (sedative-hypnotics) are three commonly-prescribed but potentially risky drug groups, prescribed alone or in combination. Physician shared-patient networks provide a unique perspective in studying physician network characteristics and structures, as well as their association with the delivery of health care. Understanding how physician shared-patient networks are related to their prescribing may inform network-based interventions targeting risky-prescribing, which is yet to be fully studied.

We investigated patient receipt of risky prescriptions and physician risky-prescribing intensity through the scope of shared-patient networks. We used retrospective Medicare insurance …

Reassessing Replication: Addressing The Replication Crisis From A Statistical Perspective, Alicia Richards Phd

Theses and Dissertations

In 2015, Open Science Framework directly replicated 100 psychology studies and found astonishingly low replication rates. Since, researchers have suggested factors that may have influenced the low rates, including the metrics used to assess replications. The definitions used to decide whether a replication study was successful all suffer from flaws. Therefore, we propose a new metric for assessing replication that can estimate the likelihood a study successfully replicated rather than forcing a binary choice and accounts for study design limitations.

Using equivalence study techniques, we first propose a new metric to assess replication, defining a successful replication as one where …

Examining The Relationship Between Stomiiform Fish Morphology And Their Ecological Traits, Mikayla L. Twiss

All HCAS Student Capstones, Theses, and Dissertations

Trait-based ecology characterizes individuals’ functional attributes to better understand and predict their interactions with other species and their environments. Utilizing morphological traits to describe functional groups has helped group species with similar ecological niches that are not necessarily taxonomically related. Within the deep-pelagic fishes, the Order Stomiiformes exhibits high morphological and species diversity, and many species undertake diel vertical migration (DVM). While the morphology and behavior of stomiiform fishes have been extensively studied and described through taxonomic assessments, the connection between their form and function regarding their DVM types, morphotypes, and daytime depth distributions is not well known. Here, three …

A Comparison Of Logistic, Ridge, And Lasso Regression With Heart Failure Risk Data: Effects Of Sample Size, Predictor Correlation, And Predictor Weight On Outcome Accuracy, Mahmoud M. Aljuhani

Electronic Theses and Dissertations

Logistic Regression (LR), LASSO regression, and RIDGE regression are standard classification techniques for predicting a dichotomous output. Since these methods are applied for similar purposes and have different features, it is crucial to evaluate the performance of these methods under different controlled conditions. With this information, researchers can apply the optimal method for specific conditions.

Following previous research, which reported the effects of conditions such as sample size and multicollinearity on the performance of the classification methods, this research focused on the effects of when sample size, level of predictor collinearity, and predictor variable weight are controlled on the performance …

Bayesian Methods For Graphical Models With Neighborhood Selection., Sagnik Bhadury

Electronic Theses and Dissertations

Graphical models determine associations between variables through the notion of conditional independence. Gaussian graphical models are a widely used class of such models, where the relationships are formalized by non-null entries of the precision matrix. However, in high-dimensional cases, covariance estimates are typically unstable. Moreover, it is natural to expect only a few significant associations to be present in many realistic applications. This necessitates the injection of sparsity techniques into the estimation method. Classical frequentist methods, like GLASSO, use penalization techniques for this purpose. Fully Bayesian methods, on the contrary, are slow because they require iteratively sampling over a quadratic …

Learning Graphical Models Of Multivariate Functional Data With Applications To Neuroimaging, Jiajing Niu

All Dissertations

This dissertation investigates the functional graphical models that infer the functional connectivity based on neuroimaging data, which is noisy, high dimensional and has limited samples. The dissertation provides two recipes to infer the functional graphical model: 1) a fully Bayesian framework 2) an end-to-end deep model.

We first propose a fully Bayesian regularization scheme to estimate functional graphical models. We consider a direct Bayesian analog of the functional graphical lasso proposed by Qiao et al. (2019).. We then propose a regularization strategy via the graphical horseshoe. We compare both Bayesian approaches to the frequentist functional graphical lasso, and compare the …

Statistical Methods For Modern Threats, Brandon Lumsden

All Dissertations

More than ever before, technology is evolving at a rapid pace across the broad spectrum of biological sciences. As data collection becomes more precise, efficient, and standardized, a demand for appropriate statistical modeling grows as well. Throughout this dissertation, we examine a variety of new age data arising from modern technology of the 21st century. We begin by employing a suite of existing statistical techniques to address research questions surrounding three medical conditions presenting in public health sciences. Here we describe the techniques used, including generalized linear models and longitudinal models, and we summarize the significant associations identified between research …

Fiscal And Monetary Policy Interactions In A Developing Economy: A Dsge-Based Evidence From Nigeria, Queen E. Oye, Philip O. Alege

CBN Journal of Applied Statistics (JAS)

This study characterizes the nature of fiscal-monetary interaction in Nigeria and gauges its macroeconomic effects by estimating a New Keynesian Dynamic Stochastic General Equilibrium (NK DSGE) model. Two policy simulations were also conducted. The first experiment considers the desirable active-passive policy mix while the second experiment ranks alternative monetary policy rules among the differing objectives of price, output and exchange rate stabilization. The study finds that fiscal and monetary policies interact as complements in an active monetary and passive fiscal policy mix over the sample period. The result from the first policy simulation reveals that the active monetary and passive …

Statistical Methods For Meta-Analysis In Large-Scale Genomic Experiments, Wimarsha Thathsarani Jayanetti

Mathematics & Statistics Theses & Dissertations

Recent developments in high throughput genomic assays have opened up the possibility of testing hundreds and thousands of genes simultaneously. With the availability of vast amounts of public databases, researchers tend to combine genomic analysis results from multiple studies in the form of a meta-analysis. Meta-analysis methods can be broadly classified into two main categories. The first approach is to combine the statistical significance (pvalues) of the genes from each individual study, and the second approach is to combine the statistical estimates (effect sizes) from the individual studies. In this dissertation, we will discuss how adherence to the standard null …

Functional Data Analysis Of Covid-19, Nichole L. Fluke

Mathematics & Statistics ETDs

This thesis deals with Functional Data Analysis (FDA) on COVID data. The Data involves counts for new COVID cases, hospitalized COVID patients, and new COVID deaths. The data used is for all the states and regions in the United States. The data starts in March 1^st, 2020 and goes through March 31^st, 2021. The FDA smooths the data and looks to see if there are similarities or differences between the states and regions in the data. The data also shows which states and regions stand out from the others and which ones are similar. Also shown …

Dynamic Prediction For Alternating Recurrent Events Using A Semiparametric Joint Frailty Model, Jaehyeon Yun

Statistical Science Theses and Dissertations

Alternating recurrent events data arise commonly in health research; examples include hospital admissions and discharges of diabetes patients; exacerbations and remissions of chronic bronchitis; and quitting and restarting smoking. Recent work has involved formulating and estimating joint models for the recurrent event times considering non-negligible event durations. However, prediction models for transition between recurrent events are lacking. We consider the development and evaluation of methods for predicting future events within these models. Specifically, we propose a tool for dynamically predicting transition between alternating recurrent events in real time. Under a flexible joint frailty model, we derive the predictive probability of …

Advanced High Dimensional Regression Techniques, Yuan Yang

All Dissertations

This dissertation focuses on developing high dimensional regression techniques to analyze large scale data using both Bayesian and frequentist approaches, motivated by data sets from various disciplines, such as public health and genetics. More specifically, Chapters 2 and Chapter 4 take a Bayesian approach to achieve modeling and parameter estimation simultaneously while Chapter 3 takes a frequentist approach. The main aspects of these techniques are that they perform variable selection and parameter estimation simultaneously, while also being easily adaptable to large-scale data. In particular, by embedding a logistic model into traditional spike and slab framework and selecting of proper prior …

Statistical Extensions Of Multi-Task Learning With Semiparametric Methods And Task Diagnostics, Nikolay Miller

Mathematics & Statistics ETDs

In this dissertation, I propose new approaches to multi-task learning, inspired by statistical model diagnostics and semiparametric and additive modeling. The newly designed additive multi-task model framework allows for flexible estimation of multi-task parametric and nonparametric effects by using an extension of the backfitting algorithm. Further, I propose new methods for statistical task diagnostics, which allow for the identification and remedy of outlier tasks, based on task-specific performance metrics and their empirical distributions. I perform a deep examination of the well-established multi-task kernel method and achieve theoretical and experimental contributions. Lastly, I propose a two-step modeling approach to multi-task modeling, …

The Short-Term Effects Of Fine Airborne Particulate Matter And Climate On Covid-19 Disease Dynamics, El Hussain Shamsa, Kezhong Zhang

Medical Student Research Symposium

Background: Despite more than 60% of the United States population being fully vaccinated, COVID-19 cases continue to spike in a temporal pattern. These patterns in COVID-19 incidence and mortality may be linked to short-term changes in environmental factors.

Methods: Nationwide, county-wise measurements for COVID-19 cases and deaths, fine-airborne particulate matter (PM_2.5), and maximum temperature were obtained from March 20, 2020 to March 20, 2021. Multivariate Linear Regression was used to analyze the association between environmental factors and COVID-19 incidence and mortality rates in each season. Negative Binomial Regression was used to analyze daily fluctuations of COVID-19 cases …

Adjusting Community Survey Data Benchmarks For External Factors, Allen Miller, Nicole M. Norelli, Robert Slater, Mingyang N. Yu

SMU Data Science Review

Abstract. Using U.S. resident survey data from the National Community Survey in combination with public data from the U.S. Census and additional sources, a Voting Regressor Model was developed to establish fair benchmark values for city performance. These benchmarks were adjusted for characteristics the city cannot easily influence that contribute to confidence in local government, such as population size, demographics, and income. This adjustment allows for a more meaningful comparison and interpretation of survey results among individual cities. Methods explored for the benchmark adjustment included cluster analysis, anomaly detection, and a variety of regression techniques, including random forest, ridge, decision …

Investigation Of The Association Of Exposures To Fire-Related Hazards With Pulmonary Function Of Firefighters, David G. Goldfarb

Dissertations and Theses

Background. Firefighters are habitually exposed to hazardous toxicants which place them at an elevated risk for numerous adverse health outcomes. An example of this is the associations observed in other works between inhalation of combustion byproducts from urban structural fires and both acute and chronic pulmonary dysfunction. To-date, the characterization of firefighters’ exposures to dangerous chemicals in smoke from non-wildfire incidents, both directly through personal monitoring and indirectly from work-related records is scarce. Prior works investigating the association between routine firefighting and pulmonary function have relied on crude metrics such as years of service and numbers of responses to …

Statistical Modeling Of Longitudinal Medical Cost Data, Shikun Wang

Dissertations and Theses (Open Access)

Projecting the future cancer care cost is critical in health economics research and policy making. An indispensable step is to estimate cost trajectories from an incident cohort of cancer patients using longitudinal medical cost data, accounting for terminal events such as death, and right censoring due to loss of follow-up. Since the cost of cancer care and survival are correlated, a scientifically meaningful quantity for inference in this context is the mean cost trajectory conditional on survival. Many standard approaches for longitudinal and survival analysis are not valid for the problem. The research for my Ph.D. dissertation consists of three …

Effect Of Monetary Policy Rate On Market Interest Rates In Nigeria: A Threshold And Nardl Approach, Oluwafemi E. Awopegba, Joseph O. Afolabi, Lydia T. Adeoye, Godwin O. Akpokodje

CBN Journal of Applied Statistics (JAS)

This study examines the effect of monetary policy rate (MPR) on market interest rates in Nigeria. For parsimony, we develop two indexes called the short-term interest rate (SINT) and Lending interest rate (LINT) to represent deposit and lending rates respectively. The nonlinear autoregressive distributed lag (NARDL) and threshold regression models are adopted. The study uses monthly data from 2002:M1 to 2019:M12. The results of the threshold regression model indicate that the degree of the effect of MPR on SINT and LINT above the estimated threshold of 11 and 13 percent respectively is greater and significant than if MPR were to …

Social Dimension Of Inclusive Growth In Ecowas: Implication For Poverty Reduction, Toriola K. Anu, Goerge O. Emmanuel, Ajayi O. Felix

CBN Journal of Applied Statistics (JAS)

This study investigates the implication of the social dimension of inclusive growth on poverty reduction in Economic Community of West African States (ECOWAS) countries. It specifically examines how social indices of inclusive growth comprising of income inequality, education, and health outcomes affect poverty reduction. The study uses a panel dataset of the six (6) lower-middle income countries in ECOWAS which was analysed via panel Difference Generalised Method of Moment (D-GMM). The results show that GDP per capita exerts significant negative effect on poverty while inequality, education and health outcomes do not show significant effect on poverty. Although, the estimates of …

Effect Of Fdi Inflows On Employment Generation In Selected Ecowas Countries: Heterogeneous Panel Analysis, Timothy A. Aderemi, Olawunmi Omitogun, Bukonla G. Osisanwo

CBN Journal of Applied Statistics (JAS)

The aim of this study is to examine the effect of FDI on employment in ECOWAS sub region between 1990 and 2019. The study utilizes a panel autoregressive distributed lag model to analyse the short run and long run relationship between FDI and employment across ECOWAS sub region. In the short run, the impact of FDI on employment is negative and statistically not significant. Meanwhile, in the long run FDI has a positive and statistically significant impact on employment rate. This implies that FDI has the capacity to generate employment in countries in ECOWAS sub region. Therefore, this study recommends …