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Hermetic On-Farm Storage For Maize Weevil Control In East Africa, Ali Yakubu, Carl J. Bern, Joel R. Coats, Theodore B. Bailey 2016 Iowa State University

Hermetic On-Farm Storage For Maize Weevil Control In East Africa, Ali Yakubu, Carl J. Bern, Joel R. Coats, Theodore B. Bailey

Carl J. Bern

Maize (Zea mays L.) consumption makes up over half of daily caloric intake of persons in East Africa and adequate supply is necessary for food security for subsistence farmers, as well as for domestic stability. Hermetic post-harvest maize storage is an attractive non-chemical control strategy for maize weevil,Sitophilus zeamais (Motsch.), which is the principal cause of insect damage to stored maize grain. Laboratory experiments were conducted on instrumented hermetic and non-hermetic containers to measure effects of temperature (10 vs. 27°C) and maize moistures (6.3 to 16%) on maize weevil biology and mortality rate, and to quantify weevil ...


Matching The Efficiency Gains Of The Logistic Regression Estimator While Avoiding Its Interpretability Problems, In Randomized Trials With Binary Outcomes, Michael Rosenblum, Jon Arni Steingrimsson 2016 Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics

Matching The Efficiency Gains Of The Logistic Regression Estimator While Avoiding Its Interpretability Problems, In Randomized Trials With Binary Outcomes, Michael Rosenblum, Jon Arni Steingrimsson

Johns Hopkins University, Dept. of Biostatistics Working Papers

Adjusting for prognostic baseline covariates can improve precision in analyzing randomized trials, leading to greater power to detect a treatment effect. For binary outcomes, a logistic regression estimator is commonly used for such adjustment. This has led to substantial efficiency gains in practice; for example, gains equivalent to reducing the required sample size by 20-28% were observed in a recent survey of traumatic brain injury trials. Robinson and Jewell (1991) proved that the logistic regression estimator is guaranteed to have equal or better asymptotic efficiency compared to the unadjusted estimator (which ignores baseline variables). Unfortunately, the logistic regression estimator has ...


Improving Precision By Adjusting For Baseline Variables In Randomized Trials With Binary Outcomes, Without Regression Model Assumptions, Jon Arni Steingrimsson, Daniel F. Hanley, Michael Rosenblum 2016 Johns Hopkins Bloomberg School of Public Health

Improving Precision By Adjusting For Baseline Variables In Randomized Trials With Binary Outcomes, Without Regression Model Assumptions, Jon Arni Steingrimsson, Daniel F. Hanley, Michael Rosenblum

Johns Hopkins University, Dept. of Biostatistics Working Papers

In randomized clinical trials with baseline variables that are prognostic for the primary outcome, there is potential to improve precision and reduce sample size by appropriately adjusting for these variables. A major challenge is that there are multiple statistical methods to adjust for baseline variables, but little guidance on which is best to use in a given context. The choice of method can have important consequences. For example, one commonly used method leads to uninterpretable estimates if there is any treatment effect heterogeneity, which would jeopardize the validity of trial conclusions. We give practical guidance on how to avoid this ...


An Exploration Of Information Exchange By Adolescents And Parents Participating In Adolescent Idiopathic Scoliosis Online Support Groups, Traci Schwieger, Shelly Campo, Keli R. Steuber, Stuart L. Weinstein, Sato Ashida 2016 University of Iowa

An Exploration Of Information Exchange By Adolescents And Parents Participating In Adolescent Idiopathic Scoliosis Online Support Groups, Traci Schwieger, Shelly Campo, Keli R. Steuber, Stuart L. Weinstein, Sato Ashida

Department of Biostatistics Publications

Background

Research indicates that healthcare providers frequently fail to adequately address patients’ health information needs. Therefore, it is not surprising that patients or parents of a sick child are seeking health information on the internet, in particular in online support groups (OSGs). In order to improve our understanding of the unmet health information needs of families dealing with adolescent idiopathic scoliosis (AIS), this study assessed and compared the types of information that adolescents and parents are seeking in OSGs.

Methods

This study used two publicly accessible AIS-related OSGs on the National Scoliosis Foundation (NSF) website that targeted those who are ...


Using Machine Learning And Natural Language Processing Algorithms To Automate The Evaluation Of Clinical Decision Support In Electronic Medical Record Systems, Donald A. Szlosek, Jonathan M. Ferretti 2016 University of Southern Maine

Using Machine Learning And Natural Language Processing Algorithms To Automate The Evaluation Of Clinical Decision Support In Electronic Medical Record Systems, Donald A. Szlosek, Jonathan M. Ferretti

eGEMs (Generating Evidence & Methods to improve patient outcomes)

Introduction: As the number of clinical decision support systems incorporated into electronic medical records increases, so does the need to evaluate their effectiveness. The use of medical record review and similar manual methods for evaluating decision rules is laborious and inefficient. Here we use machine learning and natural language processing (NLP) algorithms to accurately evaluate a clinical decision support rule through an electronic medical record system and compare it against manual evaluation.

Methods: Modeled after the electronic medical record system EPIC at Maine Medical Center, we developed a dummy dataset containing physician notes in free text for 3621 artificial patients ...


Level Of Patient-Physician Agreement In Assessment Of Change Following Conservative Rehabilitation For Shoulder Pain, Stephanie D. Moore-Reed, W. Ben Kibler, Heather M. Bush, Timothy L. Uhl 2016 California State University, Fresno

Level Of Patient-Physician Agreement In Assessment Of Change Following Conservative Rehabilitation For Shoulder Pain, Stephanie D. Moore-Reed, W. Ben Kibler, Heather M. Bush, Timothy L. Uhl

Tim L. Uhl

Background Assessment of health-related status has been shown to vary between patients and physicians, although the degree of patient–physician discordance in the assessment of the change in status is unknown.

Methods Ninety-nine patients with shoulder dysfunction underwent a standardized physician examination and completed several self-reported questionnaires. All patients were prescribed the same physical therapy intervention. Six weeks later, the patients returned to the physician, when self-report questionnaires were re-assessed and the Global Rating of Change (GROC) was completed by the patient. The physician completed the GROC retrospectively. To determine agreement between patient and physician, intra-class correlation (ICC) coefficient and ...


Multilevel Models For Longitudinal Data, Aastha Khatiwada 2016 East Tennessee State University

Multilevel Models For Longitudinal Data, Aastha Khatiwada

Electronic Theses and Dissertations

Longitudinal data arise when individuals are measured several times during an ob- servation period and thus the data for each individual are not independent. There are several ways of analyzing longitudinal data when different treatments are com- pared. Multilevel models are used to analyze data that are clustered in some way. In this work, multilevel models are used to analyze longitudinal data from a case study. Results from other more commonly used methods are compared to multilevel models. Also, comparison in output between two software, SAS and R, is done. Finally a method consisting of fitting individual models for each ...


Retention Of Mothers And Infants In The Prevention Of Mother-To-Child Transmission Of Hiv Programme Is Associated With Individual And Facility-Level Factors In Rwanda., Godfrey B Woelk, Dieudonne Ndatimana, Sally Behan, Martha Mukaminega, Epiphanie Nyirabahizi, Heather J. Hoffman, Placidie Mugwaneza, Muhayimpundu Ribakare, Anouk Amzel, B Ryan Phelps 2016 George Washington University

Retention Of Mothers And Infants In The Prevention Of Mother-To-Child Transmission Of Hiv Programme Is Associated With Individual And Facility-Level Factors In Rwanda., Godfrey B Woelk, Dieudonne Ndatimana, Sally Behan, Martha Mukaminega, Epiphanie Nyirabahizi, Heather J. Hoffman, Placidie Mugwaneza, Muhayimpundu Ribakare, Anouk Amzel, B Ryan Phelps

Epidemiology and Biostatistics Faculty Publications

OBJECTIVES: Investigate levels of retention at specified time periods along the prevention of mother-to-child transmission (PMTCT) cascade among mother-infant pairs as well as individual- and facility-level factors associated with retention.

METHODS: A retrospective cohort of HIV-positive pregnant women and their infants attending five health centres from November 2010 to February 2012 in the Option B programme in Rwanda was established. Data were collected from several health registers and patient follow-up files. Additionally, informant interviews were conducted to ascertain health facility characteristics. Generalized estimating equation methods and modelling were utilized to estimate the number of mothers attending each antenatal care visit ...


Variable Selection For Estimating The Optimal Treatment Regimes In The Presence Of A Large Number Of Covariate, Baqun Zhang, Min Zhang 2016 School of Statistics, Renmin University

Variable Selection For Estimating The Optimal Treatment Regimes In The Presence Of A Large Number Of Covariate, Baqun Zhang, Min Zhang

The University of Michigan Department of Biostatistics Working Paper Series

Most of existing methods for optimal treatment regimes, with few exceptions, focus on estimation and are not designed for variable selection with the objective of optimizing treatment decisions. In clinical trials and observational studies, often numerous baseline variables are collected and variable selection is essential for deriving reliable optimal treatment regimes. Although many variable selection methods exist, they mostly focus on selecting variables that are important for prediction (predictive variables) instead of variables that have a qualitative interaction with treatment (prescriptive variables) and hence are important for making treatment decisions. We propose a variable selection method within a general classification ...


Level Of Patient-Physician Agreement In Assessment Of Change Following Conservative Rehabilitation For Shoulder Pain, Stephanie D. Moore-Reed, W. Ben Kibler, Heather M. Bush, Timothy L. Uhl 2016 California State University, Fresno

Level Of Patient-Physician Agreement In Assessment Of Change Following Conservative Rehabilitation For Shoulder Pain, Stephanie D. Moore-Reed, W. Ben Kibler, Heather M. Bush, Timothy L. Uhl

Biostatistics Faculty Publications

Background Assessment of health-related status has been shown to vary between patients and physicians, although the degree of patient–physician discordance in the assessment of the change in status is unknown.

Methods Ninety-nine patients with shoulder dysfunction underwent a standardized physician examination and completed several self-reported questionnaires. All patients were prescribed the same physical therapy intervention. Six weeks later, the patients returned to the physician, when self-report questionnaires were re-assessed and the Global Rating of Change (GROC) was completed by the patient. The physician completed the GROC retrospectively. To determine agreement between patient and physician, intra-class correlation (ICC) coefficient and ...


Practical Targeted Learning From Large Data Sets By Survey Sampling, Patrice Bertail, Antoine Chambaz, Emilien Joly 2016 Modal'X, Université Paris Ouest Nanterre

Practical Targeted Learning From Large Data Sets By Survey Sampling, Patrice Bertail, Antoine Chambaz, Emilien Joly

U.C. Berkeley Division of Biostatistics Working Paper Series

We address the practical construction of asymptotic confidence intervals for smooth (i.e., pathwise differentiable), real-valued statistical
parameters by targeted learning from independent and identically
distributed data in contexts where sample size is so large that it poses
computational challenges. We observe some summary measure of all data and select a sub-sample from the complete data set by Poisson rejective sampling with unequal inclusion probabilities based on the summary measures. Targeted learning is carried out from the easier to handle sub-sample. We derive a central limit theorem for the targeted minimum loss estimator (TMLE) which enables the construction of the ...


Scalable Collaborative Targeted Learning For Large Scale And High-Dimensional Data, Cheng Ju, Susan Gruber, Samuel D. Lendle, Jessica M. Franklin, Richard Wyss, Sebastian Schneeweiss, Mark J. van der Laan 2016 Division of Biostatistics, University of California, Berkeley

Scalable Collaborative Targeted Learning For Large Scale And High-Dimensional Data, Cheng Ju, Susan Gruber, Samuel D. Lendle, Jessica M. Franklin, Richard Wyss, Sebastian Schneeweiss, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

The collaborative double robust targeted maximum likelihood estimator (C-TMLE) is an extension of targeted minimum loss-based estimators (TMLE) that pursues an optimal strategy for estimation of the nuisance parameter. The original implementation of C-TMLE algorithm uses a greedy forward stepwise selection procedure to construct a nested sequence of candidate nuisance parameter estimators. Cross-validation is then used to select the candidate that minimizes bias in the estimate of the target parameter, rather than basing selection on the fit of the nuisance parameter model. C-TMLE has exhibited superior relative performance in analyses of sparse data, but the time complexity of the algorithm ...


Propensity Score Prediction For Electronic Healthcare Dataset Using Super Learner And High-Dimensional Propensity Score Method, Cheng Ju, Mary Combs, Samuel D. Lendle, Jessica M. Franklin, Richard Wyss, Sebastian Schneeweiss, Mark J. van der Laan 2016 Division of Biostatistics, University of California, Berkeley

Propensity Score Prediction For Electronic Healthcare Dataset Using Super Learner And High-Dimensional Propensity Score Method, Cheng Ju, Mary Combs, Samuel D. Lendle, Jessica M. Franklin, Richard Wyss, Sebastian Schneeweiss, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

The optimal learner for prediction modeling varies depending on the underlying data-generating distribution. To select the best algorithm for a given set of data we must therefore use cross-validation to compare several candidate algorithms. Super Learner (SL) is an ensemble learning algorithm that uses cross-validation to select among a "library" of candidate algorithms. The SL is not restricted to a single prediction algorithm, but uses the strengths of a variety of learning algorithms to adapt to different datasets.
While the SL has been shown to perform well in a number of settings, it has not been evaluated in large electronic ...


Tmle For Marginal Structural Models Based On An Instrument, Boriska Toth, Mark J. van der Laan 2016 University of California, Berkeley, Division of Biostatistics

Tmle For Marginal Structural Models Based On An Instrument, Boriska Toth, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

We consider estimation of a causal effect of a possibly continuous treatment when treatment assignment is potentially subject to unmeasured confounding, but an instrumental variable is available. Our focus is on estimating heterogeneous treatment effects, so that the treatment effect can be a function of an arbitrary subset of the observed covariates. One setting where this framework is especially useful is with clinical outcomes. Allowing the causal dose-response curve to depend on a subset of the covariates, we define our parameter of interest to be the projection of the true dose-response curve onto a user-supplied working marginal structural model. We ...


Using A Data Quality Framework To Clean Data Extracted From The Electronic Health Record: A Case Study., Oliwier Dziadkowiec, Tiffany Callahan, Mustafa Ozkaynak, Blaine Reeder, John Welton 2016 University of Colorado, College of Nursing, Anschutz Medical Campus

Using A Data Quality Framework To Clean Data Extracted From The Electronic Health Record: A Case Study., Oliwier Dziadkowiec, Tiffany Callahan, Mustafa Ozkaynak, Blaine Reeder, John Welton

eGEMs (Generating Evidence & Methods to improve patient outcomes)

Objectives: Examine (1) the appropriateness of using a data quality (DQ) framework developed for relational databases as a data-cleaning tool for a dataset extracted from two EPIC databases; and (2) the differences in statistical parameter estimates on a dataset cleaned with the DQ framework and dataset not cleaned with the DQ framework.

Background: The use of data contained within electronic health records (EHRs) has the potential to open doors for a new wave of innovative research. Without adequate preparation of such large datasets for analysis, the results might be erroneous, which might affect clinical decision making or results of Comparative ...


A Powerful Statistical Framework For Generalization Testing In Gwas, With Application To The Hchs/Sol, Tamar Sofer, Ruth Heller, Marina Bogomolov, Christy L. Avery, Mariaelisa Graff, Kari E. North, Alex Reiner, Timothy A. Thornton, Kenneth Rice, Yoav Benjamini, Cathy C. Laurie, Kathleen F. Kerr 2016 University of Washington

A Powerful Statistical Framework For Generalization Testing In Gwas, With Application To The Hchs/Sol, Tamar Sofer, Ruth Heller, Marina Bogomolov, Christy L. Avery, Mariaelisa Graff, Kari E. North, Alex Reiner, Timothy A. Thornton, Kenneth Rice, Yoav Benjamini, Cathy C. Laurie, Kathleen F. Kerr

UW Biostatistics Working Paper Series

In GWAS, “generalization” is the replication of genotype-phenotype association in a population with different ancestry than the population in which it was first identified. The standard for reporting findings from a GWAS requires a two-stage design, in which discovered associations are replicated in an independent follow-up study. Current practices for declaring generalizations rely on testing associations while controlling the Family Wise Error Rate (FWER) in the discovery study, then separately controlling error measures in the follow-up study. While this approach limits false generalizations, we show that it does not guarantee control over the FWER or False Discovery Rate (FDR) of ...


Joint Modelling In Liver Transplantation, Elizabeth M. Renouf 2016 The University of Western Ontario

Joint Modelling In Liver Transplantation, Elizabeth M. Renouf

Electronic Thesis and Dissertation Repository

In the setting of liver transplantation, clinical trials and transplant registries regularly collect repeated measurements of clinical biomarkers which may be strongly associated with a time-to-event such as graft failure or disease recurrence. Multiple time-to-event outcomes are routinely collected. However, joint models are rarely used. This thesis will describe important considerations for joint modelling in the setting of liver transplantation. We will focus on transplant registry data from the United States. We develop a new tool for joint modelling in the context where a critical health event can be tracked in the longitudinal biomarker and often presents as a non-linear ...


Mapping Morels: Predicting The Locations Of Morchella Species Through Environmental Factors Using The Gis System, Emily M. Stanevicius 2016 Augustana College - Rock Island

Mapping Morels: Predicting The Locations Of Morchella Species Through Environmental Factors Using The Gis System, Emily M. Stanevicius

Celebration of Learning

Morel mushrooms, Morechella esculenta and M. deliciosa, are known delicacies across the globe, ranging from exquisite dishes in French cuisine to Eastern palates such as Japanese Matsutake. According to literature, true morels diverged as their own genus about 129 million years, again which has led to the development of more than 177 species and have been part of the human diet since their beginning. However, the elusiveness of morels has contributed to the mushrooms infamy for rarity, and has even been known to sell for more than $40 per pound. This project seeks to aid in the search for morels ...


Multiple Imputation Based Clustering Validation (Miv) For Big Longitudinal Trial Data With Missing Values In Ehealth, Zhaoyang Zhang, Hua (Julia) Fang, Honggang Wang 2016 University of Massachusetts Medical School

Multiple Imputation Based Clustering Validation (Miv) For Big Longitudinal Trial Data With Missing Values In Ehealth, Zhaoyang Zhang, Hua (Julia) Fang, Honggang Wang

Quantitative Health Sciences Publications and Presentations

Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of ...


Putting Prep Into Practice: Lessons Learned From Early-Adopting U.S. Providers' Firsthand Experiences Providing Hiv Pre-Exposure Prophylaxis And Associated Care, S. K. Calabrese, Manya Magnus, K. H. Mayer, D. S. Krakower, A. I. Eldahan, L. A. Gaston Hawkins, +5 additional authors 2016 George Washington University

Putting Prep Into Practice: Lessons Learned From Early-Adopting U.S. Providers' Firsthand Experiences Providing Hiv Pre-Exposure Prophylaxis And Associated Care, S. K. Calabrese, Manya Magnus, K. H. Mayer, D. S. Krakower, A. I. Eldahan, L. A. Gaston Hawkins, +5 Additional Authors

Epidemiology and Biostatistics Faculty Publications

Optimizing access to HIV pre-exposure prophylaxis (PrEP), an evidence-based HIV prevention resource, requires expanding healthcare providers' adoption of PrEP into clinical practice. This qualitative study explored PrEP providers' firsthand experiences relative to six commonly-cited barriers to prescription-financial coverage, implementation logistics, eligibility determination, adherence concerns, side effects, and anticipated behavior change (risk compensation)-as well as their recommendations for training PrEP-inexperienced providers. U.S.-based PrEP providers were recruited via direct outreach and referral from colleagues and other participants (2014-2015). One-on-one interviews were conducted in person or by phone, transcribed, and analyzed. The sample (n = 18) primarily practiced in the Northeastern ...


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