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The Incubation Period Of Coronavirus Disease 2019 (Covid-19) From Publicly Reported Confirmed Cases: Estimation And Application, Stephen A. Lauer, Kyra H. Grantz, Qifang Bi, Forest K. Jones, Qulu Zheng, Hannah R. Meredith, Andrew S. Azman, Nicholas G. Reich, Justin Lessler 2020 Johns Hopkins Bloomberg School of Public Health

The Incubation Period Of Coronavirus Disease 2019 (Covid-19) From Publicly Reported Confirmed Cases: Estimation And Application, Stephen A. Lauer, Kyra H. Grantz, Qifang Bi, Forest K. Jones, Qulu Zheng, Hannah R. Meredith, Andrew S. Azman, Nicholas G. Reich, Justin Lessler

Biostatistics and Epidemiology Faculty Publications Series

Background:

A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities.

Objective:

To estimate the length of the incubation period of COVID-19 and describe its public health implications.

Design:

Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020.

Setting:

News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China ...


Shrinkage Priors For Isotonic Probability Vectors And Binary Data Modeling, Philip S. Boonstra, Daniel R. Owen, Jian Kang 2020 The University Of Michigan

Shrinkage Priors For Isotonic Probability Vectors And Binary Data Modeling, Philip S. Boonstra, Daniel R. Owen, Jian Kang

The University of Michigan Department of Biostatistics Working Paper Series

This paper outlines a new class of shrinkage priors for Bayesian isotonic regression modeling a binary outcome against a predictor, where the probability of the outcome is assumed to be monotonically non-decreasing with the predictor. The predictor is categorized into a large number of groups, and the set of differences between outcome probabilities in consecutive categories is equipped with a multivariate prior having support over the set of simplexes. The Dirichlet distribution, which can be derived from a normalized cumulative sum of gamma-distributed random variables, is a natural choice of prior, but using mathematical and simulation-based arguments, we show that ...


A Modular Framework For Early-Phase Seamless Oncology Trials, Philip S. Boonstra, Thomas M. Braun, Elizabeth C. Chase 2020 The University Of Michigan

A Modular Framework For Early-Phase Seamless Oncology Trials, Philip S. Boonstra, Thomas M. Braun, Elizabeth C. Chase

The University of Michigan Department of Biostatistics Working Paper Series

Background: As our understanding of the etiology and mechanisms of cancer becomes more sophisticated and the number of therapeutic options increases, phase I oncology trials today have multiple primary objectives. Many such designs are now 'seamless', meaning that the trial estimates both the maximum tolerated dose and the efficacy at this dose level. Sponsors often proceed with further study only with this additional efficacy evidence. However, with this increasing complexity in trial design, it becomes challenging to articulate fundamental operating characteristics of these trials, such as (i) what is the probability that the design will identify an acceptable, i.e ...


Spatial And Temporal Genetic Structure Of Winter-Run Steelhead (Oncorhynchus Mykiss) Returning To The Mad River, California, Steven R. Fong 2020 Humboldt State University

Spatial And Temporal Genetic Structure Of Winter-Run Steelhead (Oncorhynchus Mykiss) Returning To The Mad River, California, Steven R. Fong

HSU theses and projects

Distinct populations of steelhead in the wild are in decline. The propagation of steelhead in hatcheries has been used to boost population numbers for recreational fisheries and for use in conservation. However, hatchery breeding practices of steelhead can result in changes in genetic structure. I investigated the genetic structure of winter-run steelhead (Oncorhynchus mykiss) returning to the Mad River, California, where a hatchery has been used enhance production for recreational fisheries since 1971. Genetic variability in Mad River steelhead was evaluated using 96 single nucleotide polymorphisms (SNPs) among 4203 individuals, including the Mad River and nearby locations, and spanning 44 ...


Nutrition And Health Status Of Hemodialysis Patients In Dhaka, Bangladesh, Tanjina Rahman 2020 Wayne State University

Nutrition And Health Status Of Hemodialysis Patients In Dhaka, Bangladesh, Tanjina Rahman

Wayne State University Dissertations

Methods to identify patients at risk for End stage renal disease (ESRD) are a high priority in Bangladesh, where kidney transplants/dialysis options are limited and costly. Every year, 35,000 to 40,000 people reach ESRD in Bangladesh, but currently available facilities can hardly accommodate only 9000 to 10,000 new patients with twice weekly dialysis and the remaining 66% have no access to any kind of renal replacement therapy (RRT) in the form of dialysis or transplantation. Nutrition is an important factor in maintaining good health of hemodialysis patients. However, data on nutritional status of Bangladeshi dialysis patients ...


Simulation Studies To Assess The Power Of Set Testing Methods For Microbiome Data, Lauren McKeen 2020 Iowa State University

Simulation Studies To Assess The Power Of Set Testing Methods For Microbiome Data, Lauren Mckeen

Creative Components

With advances in sequencing methods, the study of the microbiome has greatly increased. Microbiome data, in the form of an OTU or ASV count table, can be used to identify specific ASVs that function differently across treatment conditions. Such analysis is deemed differential abundance analysis. ASVs are grouped by their taxonomic rank, and ASVs sharing the same rank have similar biological traits. By studying groups or sets of ASVs, and identifying if the set is differentially abundant, the biological interpretation of a microbiome study is enhanced. We review current approaches in set testing methods and apply them to a microbiome ...


State Or Market? How To Effectively Decrease Alcohol-Related Crash Fatalities And Injuries, Jose I. Nazif-Muñoz, Brice Batomen, Youssef Oulhote, Arijit Nandi 2020 Université de Sherbrooke

State Or Market? How To Effectively Decrease Alcohol-Related Crash Fatalities And Injuries, Jose I. Nazif-Muñoz, Brice Batomen, Youssef Oulhote, Arijit Nandi

Biostatistics and Epidemiology Faculty Publications Series

Background It is estimated that more than 270 000 people die yearly in alcohol-related crashes globally. To tackle this burden, government interventions, such as laws which restrict blood alcohol concentration (BAC) levels and increase penalties for drunk drivers, have been implemented. The introduction of private-sector measures, such as ridesharing, is regarded as alternatives to reduce drunk driving and related sequelae. However, it is unclear whether state and private efforts complement each other to reduce this public health challenge.

Methods We conducted interrupted time-series analyses using weekly alcohol-related traffic fatalities and injuries per 1 000 000 population in three urban conglomerates ...


Coxphlb: An R Package For Analyzing Length Biased Data Under Cox Model, Chi Hyun Lee, Heng Zhou, Jing Ning, Diane D. Liu, Yu Shen 2020 University of Massachusetts Amherst

Coxphlb: An R Package For Analyzing Length Biased Data Under Cox Model, Chi Hyun Lee, Heng Zhou, Jing Ning, Diane D. Liu, Yu Shen

Biostatistics and Epidemiology Faculty Publications Series

Data subject to length-biased sampling are frequently encountered in various applications including prevalent cohort studies and are considered as a special case of left-truncated data under the stationarity assumption. Many semiparametric regression methods have been proposed for lengthbiased data to model the association between covariates and the survival outcome of interest. In this paper, we present a brief review of the statistical methodologies established for the analysis of length-biased data under the Cox model, which is the most commonly adopted semiparametric model, and introduce an R package CoxPhLb that implements these methods. Specifically, the package includes features such as fitting ...


Development Of The Gambling Disorder Identification Test: Results From An International Delphi And Consensus Process, Olof Molander, Rachel Volberg, Viktor Månsson, Kristina Sundqvist, Peter Wennberg, Anne H. Berman 2020 Karolinska Institutet

Development Of The Gambling Disorder Identification Test: Results From An International Delphi And Consensus Process, Olof Molander, Rachel Volberg, Viktor Månsson, Kristina Sundqvist, Peter Wennberg, Anne H. Berman

Biostatistics and Epidemiology Faculty Publications Series

Objectives

Diverse instruments are used to measure problem gambling and Gambling Disorder intervention outcomes. The 2004 Banff consensus agreement proposed necessary features for reporting gambling treatment efficacy. To address the challenge of including these features in a single instrument, a process was initiated to develop the Gambling Disorder Identification Test (GDIT), as an instrument analogous to the Alcohol Use Disorders Identification Test and the Drug Use Disorders Identification Test.

Methods

Gambling experts from 10 countries participated in an international two‐round Delphi (n = 61; n = 30), rating 30 items proposed for inclusion in the GDIT. Gambling researchers and clinicians from ...


An Assessment Of Convergence In The Feeding Morphology Of Xiphactinus Audax And Megalops Atlanticus Using Landmark-Based Geometric Morphometrics, Edward Chase Shelburne 2020 Fort Hays State University

An Assessment Of Convergence In The Feeding Morphology Of Xiphactinus Audax And Megalops Atlanticus Using Landmark-Based Geometric Morphometrics, Edward Chase Shelburne

Master's Theses

Convergence is an evolutionary phenomenon wherein distantly related organisms independently develop features or functional adaptations to overcome similar environmental constraints. Historically, convergence among organisms has been speculated or asserted with little rigorous or quantitative investigation. More recent advancements in systematics has allowed for the detection and study of convergence in a phylogenetic context, but this does little to elucidate convergent anatomical features in extinct taxa with poorly understood evolutionary histories. The purpose of this study is to investigate one potentially convergent system—the feeding structure of Xiphactinus audax (Teleostei: Ichthyodectiformes) and Megalops atlanticus (Teleostei: Elopiformes)—using a comparative anatomical approach ...


Power Calculation For Cross-Sectional Stepped-Wedge Cluster Randomized Trials With Binary Outcomes, Linda J. Harrison, Rui Wang 2020 Harvard TH Chan School of Public Health

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 ...


The Analysis Of Neural Heterogeneity Through Mathematical And Statistical Methods, Kyle Wendling 2020 Virginia Commonwealth University

The Analysis Of Neural Heterogeneity Through Mathematical And Statistical Methods, Kyle Wendling

Theses and Dissertations

Diversity of intrinsic neural attributes and network connections is known to exist in many areas of the brain and is thought to significantly affect neural coding. Recent theoretical and experimental work has argued that in uncoupled networks, coding is most accurate at intermediate levels of heterogeneity. I explore this phenomenon through two distinct approaches: a theoretical mathematical modeling approach and a data-driven statistical modeling approach.

Through the mathematical approach, I examine firing rate heterogeneity in a feedforward network of stochastic neural oscillators utilizing a high-dimensional model. The firing rate heterogeneity stems from two sources: intrinsic (different individual cells) and network ...


Measuring Change: Prediction Of Early Onset Sepsis, Aric Schadler 2020 University of Kentucky

Measuring Change: Prediction Of Early Onset Sepsis, Aric Schadler

Theses and Dissertations--Statistics

Sepsis occurs in a patient when an infection enters into the blood stream and spreads throughout the body causing a cascading response from the immune system. Sepsis is one of the leading causes of morbidity and mortality in today’s hospitals. This is despite published and accepted guidelines for timely and appropriate interventions for septic patients. The largest barrier to applying these interventions is the early identification of septic patients. Early identification and treatment leads to better outcomes, shorter lengths of stay, and financial savings for healthcare institutions. In order to increase the lead time in recognizing patients trending towards ...


Generalization Of Kullback-Leibler Divergence For Multi-Stage Diseases: Application To Diagnostic Test Accuracy And Optimal Cut-Points Selection Criterion, Chen Mo 2020 Georgia Southern University

Generalization Of Kullback-Leibler Divergence For Multi-Stage Diseases: Application To Diagnostic Test Accuracy And Optimal Cut-Points Selection Criterion, Chen Mo

Electronic Theses and Dissertations

The Kullback-Leibler divergence (KL), which captures the disparity between two distributions, has been considered as a measure for determining the diagnostic performance of an ordinal diagnostic test. This study applies KL and further generalizes it to comprehensively measure the diagnostic accuracy test for multi-stage (K > 2) diseases, named generalized total Kullback-Leibler divergence (GTKL). Also, GTKL is proposed as an optimal cut-points selection criterion for discriminating subjects among different disease stages. Moreover, the study investigates a variety of applications of GTKL on measuring the rule-in/out potentials in the single-stage and multi-stage levels. Intensive simulation studies are conducted to compare the ...


The Undergraduate Student’S Guide To Geometric Morphometrics, Erika Crispo 2020 Pace University

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 ...


Enhancing Models And Measurements Of Traffic-Related Air Pollutants For Health Studies Using Dispersion Modeling And Bayesian Data Fusion, Stuart A. Batterman, Veronica J. Berrocal, Chad Milando, Owais Gilani, Saravanan Arunachalam, K. Max Zhang 2020 University of Michigan - Ann Arbor

Enhancing Models And Measurements Of Traffic-Related Air Pollutants For Health Studies Using Dispersion Modeling And Bayesian Data Fusion, Stuart A. Batterman, Veronica J. Berrocal, Chad Milando, Owais Gilani, Saravanan Arunachalam, K. Max Zhang

Faculty Journal Articles

Research Report 202 describes a study led by Dr. Stuart Batterman at the University of Michigan, Ann Arbor and colleagues. The investigators evaluated the ability to predict traffic-related air pollution using a variety of methods and models, including a line source air pollution dispersion model and sophisticated spatiotemporal Bayesian data fusion methods. Exposure assessment for traffic-related air pollution is challenging because the pollutants are a complex mixture and vary greatly over space and time. Because extensive direct monitoring is difficult and expensive, a number of modeling approaches have been developed, but each model has its own limitations and errors.

Dr ...


Distribution Of Human Exposure To Ozone During Commuting Hours In Connecticut Using The Cellular Device Network, Owais Gilani, Simon Urbanek, Michael J. Kane 2020 Bucknell University

Distribution Of Human Exposure To Ozone During Commuting Hours In Connecticut Using The Cellular Device Network, Owais Gilani, Simon Urbanek, Michael J. Kane

Faculty Journal Articles

Epidemiologic studies have established associations between various air pollutants and adverse health outcomes for adults and children. Due to high costs of monitoring air pollutant concentrations for subjects enrolled in a study, statisticians predict exposure concentrations from spatial models that are developed using concentrations monitored at a few sites. In the absence of detailed information on when and where subjects move during the study window, researchers typically assume that the subjects spend their entire day at home, school, or work. This assumption can potentially lead to large exposure assignment bias. In this study, we aim to determine the distribution of ...


Lifestyle Factors And Social Determinants As Predictors Of Weight/Body Mass Index, Uthman Alhaji Baba 2020 Walden University

Lifestyle Factors And Social Determinants As Predictors Of Weight/Body Mass Index, Uthman Alhaji Baba

Walden Dissertations and Doctoral Studies

Obesity is a major public health concern that includes the risk of developing cardiovascular disease and premature death in adults. Previous studies have established the relationship between gender, educational level, household income and respondents’ weight but additional research is needed to factor the nature of education in relation to gender differences, diet, and other important behavioral mediators such as social determinants. The purpose of this quantitative cross-sectional study is to determine the extent to which frequency of physical activity, household income, social determinants of health (money for balanced meals, finances at the end of month, and poor mental health), respondent ...


Nonparametric Misclassification Simulation And Extrapolation Method And Its Application, Congjian Liu 2020 Georgia Southern University

Nonparametric Misclassification Simulation And Extrapolation Method And Its Application, Congjian Liu

Electronic Theses and Dissertations

The misclassification simulation extrapolation (MC-SIMEX) method proposed by Küchenho et al. is a general method of handling categorical data with measurement error. It consists of two steps, the simulation and extrapolation steps. In the simulation step, it simulates observations with varying degrees of measurement error. Then parameter estimators for varying degrees of measurement error are obtained based on these observations. In the extrapolation step, it uses a parametric extrapolation function to obtain the parameter estimators for data with no measurement error. However, as shown in many studies, the parameter estimators are still biased as a result of the parametric extrapolation ...


Multiple Imputation Using Influential Exponential Tilting In Case Of Non-Ignorable Missing Data, Kavita Gohil 2020 Jiann Ping HSU College of Public Health

Multiple Imputation Using Influential Exponential Tilting In Case Of Non-Ignorable Missing Data, Kavita Gohil

Electronic Theses and Dissertations

Modern research strategies rely predominantly on three steps, data collection, data analysis, and inference. In research, if the data is not collected as designed, researchers may face challenges of having incomplete data, especially when it is non-ignorable. These situations affect the subsequent steps of evaluation and make them difficult to perform. Inference with incomplete data is a challenging task in data analysis and clinical trials when missing data related to the condition under the study. Moreover, results obtained from incomplete data are prone to biases. Parameter estimation with non-ignorable missing data is even more challenging to handle and extract useful ...


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