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Mechanistic Mathematical Models: An Underused Platform For Hpv Research, Marc Ryser, Patti Gravitt, Evan R. Myers 2017 George Washington University

Mechanistic Mathematical Models: An Underused Platform For Hpv Research, Marc Ryser, Patti Gravitt, Evan R. Myers

Global Health Faculty Publications

Health economic modeling has become an invaluable methodology for the design and evaluation of clinical and public health interventions against the human papillomavirus (HPV) and associated diseases. At the same time, relatively little attention has been paid to a different yet complementary class of models, namely that of mechanistic mathematical models. The primary focus of mechanistic mathematical models is to better understand the intricate biologic mechanisms and dynamics of disease. Inspired by a long and successful history of mechanistic modeling in other biomedical fields, we highlight several areas of HPV research where mechanistic models have the potential to advance the ...


A Distribution Of The First Order Statistic When The Sample Size Is Random, Vincent Z. Forgo Mr 2017 East Tennessee State University

A Distribution Of The First Order Statistic When The Sample Size Is Random, Vincent Z. Forgo Mr

Electronic Theses and Dissertations

Statistical distributions also known as probability distributions are used to model a random experiment. Probability distributions consist of probability density functions (pdf) and cumulative density functions (cdf). Probability distributions are widely used in the area of engineering, actuarial science, computer science, biological science, physics, and other applicable areas of study. Statistics are used to draw conclusions about the population through probability models. Sample statistics such as the minimum, first quartile, median, third quartile, and maximum, referred to as the five-number summary, are examples of order statistics. The minimum and maximum observations are important in extreme value theory. This paper will ...


Implementing Propensity Score Matching With Network Data: The Effect Of Gatt On Bilateral Trade, Luca De Benedictis, Bruno Arpino, Alessandra Mattei 2017 Universitat Pompeu Fabra

Implementing Propensity Score Matching With Network Data: The Effect Of Gatt On Bilateral Trade, Luca De Benedictis, Bruno Arpino, Alessandra Mattei

Luca De Benedictis

Motivated by the evaluation of the causal effect of the General Agreement on Tariffs and Trade on bilateral international trade flows, we investigate the role of network structure in propensity score matching under the assumption of strong ignorability. We study the sensitivity of causal inference with respect to the presence of characteristics of the network in the set of confounders conditional on which strong ignorability is assumed to hold. We find that estimates of the average causal effect are highly sensitive to the presence of node-level network statistics in the set of confounders. Therefore, we argue that estimates may suffer ...


Sas Macro: Generalized Mcnemar's Test For Homogeneity Of The Marginal Distributions, Zhao Yang 2017 Quintiles Inc

Sas Macro: Generalized Mcnemar's Test For Homogeneity Of The Marginal Distributions, Zhao Yang

Zhao (Tony) Yang, Ph.D.

This is the updated SAS macro with the improvement to address the issues of many empty cell, a real data example was given in the program.


Lies, Statistics, Mathematics And The Truth, Nathan L. Tintle 2017 Dordt College

Lies, Statistics, Mathematics And The Truth, Nathan L. Tintle

Faculty Work: Comprehensive List

"Recognizing a key distinction between mathematics and statistics is helpful in understanding how we know if a statement is true."

Posting about deductive and inductive reasoning­­­­­­­­ from In All Things - an online hub committed to the claim that the life, death, and resurrection of Jesus Christ has implications for the entire world.

http://inallthings.org/lies-statistics-mathematics-and-the-truth/


Another Friday Test, Sid Marchseventeen 2017 Selected Works

Another Friday Test, Sid Marchseventeen

Sid Marchseventeen

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Test Demo Search - Two, Sid Threemarchsixteen 2017 Selected Works

Test Demo Search - Two, Sid Threemarchsixteen

Sid Threemarchsixteen

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Test Demo Search - One, Sid Threemarchsixteen 2017 Selected Works

Test Demo Search - One, Sid Threemarchsixteen

Sid Threemarchsixteen

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Massively Parallel Approximate Gaussian Process Regression, Robert B. Gramacy, Jarad Niemi, Robin M. Weiss 2017 University of Chicago

Massively Parallel Approximate Gaussian Process Regression, Robert B. Gramacy, Jarad Niemi, Robin M. Weiss

Jarad Niemi

We explore how the big-three computing paradigms---symmetric multiprocessor, graphical processing units (GPUs), and cluster computing---can together be brought to bear on large-data Gaussian processes (GP) regression problems via a careful implementation of a newly developed local approximation scheme. Our methodological contribution focuses primarily on GPU computation, as this requires the most care and also provides the largest performance boost. However, in our empirical work we study the relative merits of all three paradigms to determine how best to combine them. The paper concludes with two case studies. One is a real data fluid-dynamics computer experiment which benefits from the local ...


Empirical Bayes Analysis Of Rna-Seq Data For Detection Of Gene Expression Heterosis, Jarad Niemi, Eric Mittman, Will Landau, Dan Nettleton 2017 Iowa State University

Empirical Bayes Analysis Of Rna-Seq Data For Detection Of Gene Expression Heterosis, Jarad Niemi, Eric Mittman, Will Landau, Dan Nettleton

Jarad Niemi

An important type of heterosis, known as hybrid vigor, refers to the enhancements in the phenotype of hybrid progeny relative to their inbred parents. Although hybrid vigor is extensively utilized in agriculture, its molecular basis is still largely unknown. In an effort to understand phenotypic heterosis at the molecular level, researchers are measuring transcript abundance levels of thousands of genes in parental inbred lines and their hybrid offspring using RNA sequencing (RNA-seq) technology. The resulting data allow researchers to search for evidence of gene expression heterosis as one potential molecular mechanism underlying heterosis of agriculturally important traits. The null hypotheses ...


Interweaving Markov Chain Monte Carlo Strategies For Efficient Estimation Of Dynamic Linear Models, Matthew Simpson, Jarad Niemi, Vivekananda Roy 2017 University of Missouri

Interweaving Markov Chain Monte Carlo Strategies For Efficient Estimation Of Dynamic Linear Models, Matthew Simpson, Jarad Niemi, Vivekananda Roy

Jarad Niemi

In dynamic linear models (DLMs) with unknown fixed parameters, a standard Markov chain Monte Carlo (MCMC) sampling strategy is to alternate sampling of latent states conditional on fixed parameters and sampling of fixed parameters conditional on latent states. In some regions of the parameter space, this standard data augmentation (DA) algorithm can be inefficient. To improve efficiency, we apply the interweaving strategies of Yu and Meng to DLMs. For this, we introduce three novel alternative DAs for DLMs: the scaled errors, wrongly scaled errors, and wrongly scaled disturbances. With the latent states and the less well known scaled disturbances, this ...


Estimation And Prediction In Spatial Models With Block Composite Likelihoods, Jo Eidsvik, Benjamin A. Shaby, Brian J. Reich, Matthew Wheeler, Jarad Niemi 2017 University of Trondeim

Estimation And Prediction In Spatial Models With Block Composite Likelihoods, Jo Eidsvik, Benjamin A. Shaby, Brian J. Reich, Matthew Wheeler, Jarad Niemi

Jarad Niemi

This article develops a block composite likelihood for estimation and prediction in large spatial datasets. The composite likelihood (CL) is constructed from the joint densities of pairs of adjacent spatial blocks. This allows large datasets to be split into many smaller datasets, each of which can be evaluated separately, and combined through a simple summation. Estimates for unknown parameters are obtained by maximizing the block CL function. In addition, a new method for optimal spatial prediction under the block CL is presented. Asymptotic variances for both parameter estimates and predictions are computed using Godambe sandwich matrices. The approach considerably improves ...


Phylogenies, The Comparative Method, And The Conflation Of Tempo And Mode, Antigoni Kaliontzopoulou, Dean C. Adams 2017 CIBIO/InBio, Vairão, Portugal

Phylogenies, The Comparative Method, And The Conflation Of Tempo And Mode, Antigoni Kaliontzopoulou, Dean C. Adams

Dean C. Adams

The comparison of mathematical models that represent alternative hypotheses about the tempo and mode of evolutionary change is a common approach for assessing the evolutionary processes underlying phenotypic diversification. However, because model parameters are estimated simultaneously, they are inextricably linked, such that changes in tempo, the pace of evolution, and mode, the manner in which evolution occurs, may be difficult to assess separately. This may potentially complicate biological interpretation, but the extent to which this occurs has not yet been determined. In this study, we examined 160 phylogeny × trait empirical datasets, and conducted extensive numerical phylogenetic simulations, to investigate the ...


Evaluating Modularity In Morphometric Data: Challenges With The Rv Coefficient And A New Test Measure, Dean C. Adams 2017 Iowa State University

Evaluating Modularity In Morphometric Data: Challenges With The Rv Coefficient And A New Test Measure, Dean C. Adams

Dean C. Adams

1: Modularity describes the case where patterns of trait covariation are unevenly dispersed across traits. Specifically, trait correlations are high and concentrated within subsets of variables (modules), but the correlations between traits across modules are relatively weaker. For morphometric datasets, hypotheses of modularity are commonly evaluated using the RV coefficient, an association statistic used in a wide variety of fields. 2: In this article I explore the properties of the RV coefficient using simulated data sets. Using data drawn from a normal distribution where the data were neither modular nor integrated in structure, I show that the RV coefficient is ...


Application Of Inverse Problems In Imaging, Xiaoyue Luo 2017 Linfield College

Application Of Inverse Problems In Imaging, Xiaoyue Luo

Post-Grant Reports

In this project, we studied how to enhance image quality by denoising and deblurring a given image mathematically. We compared some existing state-of-the-art methods for image denoising and deblurring. We implemented the algorithms numerically using Matlab.

We studied the possibility of combining statistical analysis with the traditional image restoration methods including using wavelets and framelets and we derived some encouraging preliminary results.

My research student Alleta Maier gave a sequence of talks on the project including the Pacific Northwest Mathematical Association of America conference at Oregon State University in April, 2016; Linfield College Taylor Series in March, 2016, and Linfield ...


Session D-5: Informal Comparative Inference: What Is It?, Karen Togliatti 2017 Illinois Mathematics and Science Academy

Session D-5: Informal Comparative Inference: What Is It?, Karen Togliatti

Professional Learning Day

Come and experience a hands-on task that has middle-school students grapple with informal inferential reasoning. Three key principles of informal inference – data as evidence, probabilistic language, and generalizing ‘beyond the data’ will be discussed as students build and analyze distributions to answer the question, “Does hand dominance play a role in throwing accuracy?” Connections to the CCSSM statistics standards for middle-school will be highlighted.


Session D-1: Lies, Damn Lies, And Statistics, Peter Dong, Joseph Traina 2017 Illinois Mathematics and Science Academy

Session D-1: Lies, Damn Lies, And Statistics, Peter Dong, Joseph Traina

Professional Learning Day

The crucial and sometimes difficult areas of data analysis and statistics can be made clearer by looking at examples of how they can be done badly - examples which, unfortunately, are easy to find. We share our experience teaching a short course which examines disingenuous graphs, biased surveys, deliberately misworded statements, and other methods of misrepresenting data. The negative examples provide an opportunity to discuss how statistics should properly be done, and explain what can happen when statistics are used incorrectly. We include a discussion of the failure of polls to predict the outcome of the presidential election.


Effect Of Immediate Initiation Of Antiretroviral Therapy On Risk Of Severe Bacterial Infections In Hiv-Positive People With Cd4 Cell Counts Of More Than 500 Cells Per Μl: Secondary Outcome Results From A Randomised Controlled Trial., Jemma O'Connor, Michael J Vjecha, Andrew N Phillips, Brian Angus, David Cooper, Fred Gordin, +several additional authors 2017 George Washington University

Effect Of Immediate Initiation Of Antiretroviral Therapy On Risk Of Severe Bacterial Infections In Hiv-Positive People With Cd4 Cell Counts Of More Than 500 Cells Per Μl: Secondary Outcome Results From A Randomised Controlled Trial., Jemma O'Connor, Michael J Vjecha, Andrew N Phillips, Brian Angus, David Cooper, Fred Gordin, +Several Additional Authors

Medicine Faculty Publications

BACKGROUND: The effects of antiretroviral therapy on risk of severe bacterial infections in people with high CD4 cell counts have not been well described. In this study, we aimed to quantify the effects of immediate versus deferred ART on the risk of severe bacterial infection in people with high CD4 cell counts in a preplanned analysis of the START trial.

METHODS: The START trial was a randomised controlled trial in ART-naive HIV-positive patients with CD4 cell count of more than 500 cells per μL assigned to immediate ART or deferral until their CD4 cell counts were lower than 350 cells ...


A Statistical Method For The Conservative Adjustment Of False Discovery Rate (Q-Value), Yinglei Lai 2017 George Washington University

A Statistical Method For The Conservative Adjustment Of False Discovery Rate (Q-Value), Yinglei Lai

Epidemiology and Biostatistics Faculty Publications

Background

q-value is a widely used statistical method for estimating false discovery rate (FDR), which is a conventional significance measure in the analysis of genome-wide expression data. q-value is a random variable and it may underestimate FDR in practice. An underestimated FDR can lead to unexpected false discoveries in the follow-up validation experiments. This issue has not been well addressed in literature, especially in the situation when the permutation procedure is necessary for p-value calculation.

Results

We proposed a statistical method for the conservative adjustment of q-value. In practice, it is usually necessary to calculate p ...


An Online Educational Program Improves Pediatric Oncology Nurses’ Knowledge, Attitudes, And Spiritual Care Competence, Cheryl L. Petersen, Margaret Callahan, Donna O. McCarthy, Ronda G. Hughes, Rosemary White-Traut, Naveen K. Bansal 2017 Marquette University

An Online Educational Program Improves Pediatric Oncology Nurses’ Knowledge, Attitudes, And Spiritual Care Competence, Cheryl L. Petersen, Margaret Callahan, Donna O. Mccarthy, Ronda G. Hughes, Rosemary White-Traut, Naveen K. Bansal

MSCS Faculty Research and Publications

This study evaluated the potential impact of an online spiritual care educational program on pediatric nurses’ attitudes toward and knowledge of spiritual care and their competence to provide spiritual care to children with cancer at the end of life. It was hypothesized that the intervention would increase nurses’ positive attitudes toward and knowledge of spiritual care and increase nurses’ level of perceived spiritual care competence. A positive correlation was expected between change in nurses’ perceived attitudes toward and knowledge of spiritual care and change in nurses’ perceived spiritual care competence. A prospective, longitudinal design was employed, and analyses included one-way ...


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