Statistics and Probability Commons^™

Effects Of Quantitative Literacy On Healthcare Decision-Making: An Aural Context, Robert G. Root, Sonia Bhala

Numeracy

We propose a relationship between sensory modality, numerical formatting, and performance on a survey simulating healthcare decision-making. We examine the current literature on aural health literacy, and specifically aural literacy coupled with health numeracy. We then create a survey instrument called the Bhala test for this purpose and demonstrate that it is moderately internally consistent and provides results that correlate with the NUMi assessment, a widely accepted measure of health numeracy. The quantitative information provided in the Bhala test has two treatments, percentage and natural frequency formats, in an effort to determine which format is easier for subjects to use …

Estimating Marginal Hazard Ratios By Simultaneously Using A Set Of Propensity Score Models: A Multiply Robust Approach, Di Shu, Peisong Han, Rui Wang, Sengwee Toh

Harvard University Biostatistics Working Paper Series

The inverse probability weighted Cox model is frequently used to estimate marginal hazard ratios. Its validity requires a crucial condition that the propensity score model is correctly specified. To provide protection against misspecification of the propensity score model, we propose a weighted estimation method rooted in empirical likelihood theory. The proposed estimator is multiply robust in that it is guaranteed to be consistent when a set of postulated propensity score models contains a correctly specified model. Our simulation studies demonstrate satisfactory finite sample performance of the proposed method in terms of consistency and efficiency. We apply the proposed method to …

Using Hac Estimators For Intervention Analysis, Ashok K. Singh, Rohan J. Dalpatadu

Hospitality Faculty Research

The purpose of this article is to present an alternative method for intervention analysis of time series data that is simpler to use than the traditional method of fitting an explanatory Autoregressive Integrated Moving Average (ARIMA) model. Time series regression analysis is commonly used to test the effect of an event on a time series. An econometric modeling method, which uses a heteroskedasticity and autocorrelation consistent (HAC) estimator of the covariance matrix instead of fitting an ARIMA model, is proposed as an alternative. The method of parametric bootstrap is used to compare the two approaches for intervention analysis. The results …

Hypercholesterolemia Accelerates Both The Initiation And Progression Of Angiotensin Ii-Induced Abdominal Aortic Aneurysms, Jing Liu, Hisashi Sawada, Deborah A. Howatt, Jessica J. Moorleghen, Olga A. Vsevolozhskaya, Alan Daugherty, Hong S. Lu

Pharmacology and Nutritional Sciences Faculty Publications

Objective: This study determined whether hypercholesterolemia would contribute to both the initiation and progression of angiotensin (Ang)II-induced abdominal aortic aneurysms (AAAs) in mice.

Methods and Results: To determine whether hypercholesterolemia accelerates the initiation of AAAs, male low-density lipoprotein (LDL) receptor -/- mice were either fed one week of Western diet prior to starting AngII infusion or initiated Western diet one week after starting AngII infusion. During the first week of AngII infusion, mice fed normal diet had less luminal expansion of the suprarenal aorta compared to those initiated Western diet after the first week of AngII infusion. The two groups …

Estimation Of Conditional Power For Cluster-Randomized Trials With Interval-Censored Endpoints, Kaitlyn Cook, Rui Wang

Harvard University Biostatistics Working Paper Series

Cluster-randomized trials (CRTs) of infectious disease preventions often yield correlated, interval-censored data: dependencies may exist between observations from the same cluster, and event occurrence may be assessed only at intermittent clinic visits. This data structure must be accounted for when conducting interim monitoring and futility assessment for CRTs. In this article, we propose a flexible framework for conditional power estimation when outcomes are correlated and interval-censored. Under the assumption that the survival times follow a shared frailty model, we first characterize the correspondence between the marginal and cluster-conditional survival functions, and then use this relationship to semiparametrically estimate the cluster-specific …

Quantitative Model For Setting Manufacturer's Suggested Retail Price, Peter Byrd, Jonathan Knowles, Dmitry Andreev, Jacob Turner, Brian Mente, Laroux Wallace

SMU Data Science Review

In this paper, we present a quantitative approach to model the manufacturer’s suggested retail price (MSRP) for children’s doll- houses and establish relationships among key features that contribute most to establishing MSRP. Determination of the MSRP is a critical step in how consumers respond with their wallets when purchasing an item. KidKraft, a global leader in toys and juvenile products, sets MSRP subjectively using product experts. The process is arduous and time consuming requiring the focus of specialized resources and knowledge of the interaction between key attributes and their impact on consumer value. An accurate prediction of MSRP during the …

Mapping Relationships And Positions Of Objects In Images Using Mask And Bounding Box Data, Jaime M. Villanueva Jr, Anantharam Subramanian, Vishal Ahir, Andrew Pollock

SMU Data Science Review

In this paper we present novel methods for automatically annotating images with relationship and position tags that are derived using mask and bounding box data. A Mask Region-based Convolutional Neural Network (Mask R-CNN) is used as the foundation for the ob- ject detection process. The relationships are found by manipulating the bounding box and mask segmentation outputs of a Mask R-CNN. The absolute positions, the positions of the objects relative to the image, and the relative positions, the positions of objects relative to the other objects, are then associated with the images as annotations that are out- put in order …

Gradient Boosting For Survival Analysis With Applications In Oncology, Nam Phuong Nguyen

USF Tampa Graduate Theses and Dissertations

Cancer is one of the most deadly diseases that the world has been fighting against over decades. An enormous number of research has been conducted, via a wide scale of approaches, raging from genetic analysis to mathematical modeling. Survival analysis is a well-performed methodology frequently used to estimate the survival probability of a patient. Although there has been a large number of methods for survival analysis, efficient exploration of a high-dimensional feature space has been challenging due to its computational cost and complexity. This thesis adapts the component-wise gradient boosting algorithms for cancer survival analysis, and also proposes a new …

Bayesian Analysis Of The Epsilon Skew Exponential Power Distribution, Michael Ghebremeskel Weldensea

Theses and Dissertations

The Epsilon Skew Exponential Power Distribution (ESEP) that was introduced by Elsalloukh et al. (2005) is an asymmetric distribution used for modeling asymmetric data. The ESEP includes Normal, Laplace, Epsilon Skew Normal (ESN), and Epsilon Skew Laplace (ESL) as particular cases, Elsalloukh et al. (2005). In the present study, since the ESEP distribution encompasses members with skewed and symmetric distributions, we perform and investigate the Bayesian analysis of this distribution using the methods of latent variables and uniform scale mixture for implementing the most common Markov chain Monte Carlo (MCMC) algorithm known as Gibbs sampling. Furthermore, we develop the posterior …

The Undergraduate Student’S Guide To Geometric Morphometrics, Erika Crispo

Open Educational Resources

Embarking on a new research endeavor can be a daunting task. User guides, books, and published articles are written for an audience that already has some background experience in the field. Undergraduate students like you, who are at the very beginning of their research careers, often struggle to make sense of these documents. Furthermore, students like you often attempt to do so while balancing heavy course loads. Thus, I have written this document to help ease the burden so that you have more time to ponder the interesting scientific questions instead of digging through pages upon pages of documentation. I …

Playfair's Introduction Of Bar And Pie Charts To Represent Data, Diana White, River Bond, Joshua Eastes, Negar Janani

Statistics and Probability

No abstract provided.

Randomization-Based Confidence Intervals For Cluster Randomized Trials, Dustin J. Rabideau, Rui Wang

Harvard University Biostatistics Working Paper Series

In a cluster randomized trial (CRT), groups of people are randomly assigned to different interventions. Existing parametric and semiparametric methods for CRTs rely on distributional assumptions or a large number of clusters to maintain nominal confidence interval (CI) coverage. Randomization-based inference is an alternative approach that is distribution-free and does not require a large number of clusters to be valid. Although it is well-known that a CI can be obtained by inverting a randomization test, this requires randomization testing a non-zero null hypothesis, which is challenging with non-continuous and survival outcomes. In this paper, we propose a general method for …

Power Calculation For Cross-Sectional Stepped-Wedge Cluster Randomized Trials With Binary Outcomes, Linda J. Harrison, Rui Wang

Harvard University Biostatistics Working Paper Series

Power calculation for stepped-wedge cluster randomized trials (SW-CRTs) presents unique challenges, beyond those of standard cluster randomized trials (CRTs), due to the need to consider temporal within cluster correlations and background period effects. To date, power calculation methods specific to SW-CRTs have primarily been developed under a linear model. When the outcome is binary, the use of a linear model corresponds to assessing a prevalence difference; yet trial analysis often employs a non-linear link function. We assess power for cross-sectional SW-CRTs under a logistic model fitted by generalized estimating equations. Firstly, under an exchangeable correlation structure, we show the power …

Markov Chain Epidemic Models And Parameter Estimation, Oluwatobiloba Ige

Theses, Dissertations and Capstones

Over the years, various parts of the world have experienced disease outbreaks. Mathematical models are used to describe these outbreaks. We study the transmission of disease in simple cases of disease outbreaks by using compartmental models with Markov chains. First, we explore the formulation of compartmental SIS (Susceptible-Infectious-Susceptible) and SIR (Susceptible-Infectious-Recovered) disease models. These models are the basic building blocks of other compartmental disease models. Second, we build SIS and SIR disease models using both discrete and continuous time Markov chains. In discrete time models, transmission occurs at fixed time steps, and in continuous time models, transmission may occur at …

Sample Size Calculation And Blinded Recalculation For Analysis Of Covariance Models With Multiple Random Covariates, Georg Zimmermann, Meinhard Kieser, Arne C. Bathke

Statistics Faculty Publications

When testing for superiority in a parallel-group setting with a continuous outcome, adjusting for covariates is usually recommended. For this purpose, the analysis of covariance is frequently used, and recently several exact and approximate sample size calculation procedures have been proposed. However, in case of multiple covariates, the planning might pose some practical challenges and pitfalls. Therefore, we propose a method, which allows for blinded re-estimation of the sample size during the course of the trial. Simulations confirm that the proposed method provides reliable results in many practically relevant situations, and applicability is illustrated by a real-life data example.

Representing And Interpreting Data From Playfair, Diana White, River Bond, Joshua Eastes, Negar Janani

Statistics and Probability

No abstract provided.

Association Between Baseline Abundance Of Peptoniphilus, A Gram-Positive Anaerobic Coccus, And Wound Healing Outcomes Of Dfus, Kyung R. Min, Adriana Galvis, Katherine L. Baquerizo Nole, Rohita Sinha, Jennifer Clarke, Robert S. Kirsner, Dragana Ajdic

Department of Statistics: Faculty Publications

Diabetic foot ulcers (DFUs) lead to nearly 100,000 lower limb amputations annually in the United States. DFUs are colonized by complex microbial communities, and infection is one of the most common reasons for diabetes-related hospitalizations and amputations. In this study, we examined how DFU microbiomes respond to initial sharp debridement and off- loading and how the initial composition associates with 4 week healing outcomes. We employed 16S rRNA next generation sequencing to perform microbial profiling on 50 sam- ples collected from 10 patients with vascularized neuropathic DFUs. Debrided wound sam- ples were obtained at initial visit and after one week …

Representation Of Features As Images With Neighborhood Dependencies For Compatibility With Convolutional Neural Networks, Omid Bazgir, Ruibo Zhang, Saugato Rahman Dhruba, Raziur Rahman, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive modeling using features without spatial correlations. We present a feature representation approach termed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) to arrange high-dimensional vectors in a compact image form conducible for CNN-based deep learning. We consider the similarities between features to generate a concise feature map in the form of a two-dimensional image by minimizing the pairwise distance values following a Bayesian Metric Multidimensional Scaling Approach. We hypothesize that this approach enables embedded feature extraction and, integrated …

Perceived Neighborhood: Preferences Versus Actualities, Saeed Moradi, Ali Nejat, Da Hu, Souparno Ghosh

Department of Statistics: Faculty Publications

Housing recovery plays a key role in the overall restoration of a community. A multitude of factors affect housing recovery, many of which are associated with interactions of residents with their perceived neighborhoods. Targeting perceived neighborhoods rather than administratively defined measures of land helps with devising recovery plans that could better address social preferences of the residents. However, such measures are commonly subject to collection of information via expensive and time-consuming surveys. The current research aims to contribute to the domain by exploring the relationship between perception of households of their neighborhood anchors (perceived anchors) and the anchors that exist …

In Praise Of Partially Interpretable Predictors, Tri Le, Bertrand S. Clarke

Department of Statistics: Faculty Publications

Often there is an uninterpretable model that is statistically as good as, if not better than, a successful interpretable model. Accordingly, if one restricts attention to interpretable models, then one may sacrifice predictive power or other desirable properties. A minimal condition for an interpretable, usually parametric, model to be better than another model is that the first should have smallermean-squared error or integratedmean-squared error.We show through a series of examples that this is often not the case and give the asymptotic forms of a variety of interpretable, partially interpretable, and noninterpretable methods. We find techniques that combine aspects of both …