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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 ...


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

Johns Hopkins University, Dept. of Biostatistics Working Papers

Background: A recent guideline issued by the the European Medicines Agency discusses adjustment for prognostic baseline variables to improve precision and power in randomized trials.They state ``in case of a strong or moderate association between a baseline covariate(s) and the primary outcome measure, adjustment for such covariate(s) generally improves the efficiency of the analysis and avoids conditional bias from chance covariate imbalance.'' A challenge is that there are multiple statistical methods for adjusting for baseline variables, and little guidance on which to use. We investigate the pros and cons of two such adjustment methods.

Methods: We compare ...


Correction Of Verication Bias Using Log-Linear Models For A Single Binaryscale Diagnostic Tests, Haresh Rochani, Hani M. Samawi, Robert L. Vogel, Jingjing Yin 2016 Georgia Southern University

Correction Of Verication Bias Using Log-Linear Models For A Single Binaryscale Diagnostic Tests, Haresh Rochani, Hani M. Samawi, Robert L. Vogel, Jingjing Yin

Haresh Rochani

In diagnostic medicine, the test that determines the true disease status without an error is referred to as the gold standard. Even when a gold standard exists, it is extremely difficult to verify each patient due to the issues of costeffectiveness and invasive nature of the procedures. In practice some of the patients with test results are not selected for verification of the disease status which results in verification bias for diagnostic tests. The ability of the diagnostic test to correctly identify the patients with and without the disease can be evaluated by measures such as sensitivity, specificity and predictive ...


How Long Does That 10-Year Smoke Alarm Really Last? A Survival Analysis Of Smoke Alarms Installed Through The Saife Program In Rural Georgia, Haresh Rochani, Valamar Malika Reagon, Steve Davidson 2016 Georgia Southern University

How Long Does That 10-Year Smoke Alarm Really Last? A Survival Analysis Of Smoke Alarms Installed Through The Saife Program In Rural Georgia, Haresh Rochani, Valamar Malika Reagon, Steve Davidson

Haresh Rochani

Background: When functioning properly, a smoke alarm alerts individuals in the residence that smoke is near the alarm. Smoke alarms serve as a primary prevention mechanism to abate morbidity and mortality related to residential fires. Methods: Using survival analysis, we examined the length of operability of 10-year lithium battery powered smoke alarms installed through the Georgia Public Health/CDC SAIFE program in Moultrie, Georgia. Attempts were made to reach all homes in the city limits. The premise of the study is that geographic clusters (in the case of Moultrie city quadrants) are associated with decreases in the length of time ...


Initiation And Early Development Of Fiber In Wild And Cultivated Cotton, Kara M. Butterworth, Dean C. Adams, Harry T. Horner, Jonathan F. Wendel 2016 Iowa State University

Initiation And Early Development Of Fiber In Wild And Cultivated Cotton, Kara M. Butterworth, Dean C. Adams, Harry T. Horner, Jonathan F. Wendel

Harry Horner

Cultivated cotton fiber has undergone transformation from short, coarse fibers found in progenitor wild species to economically important, long, fine fibers grown globally. Morphological transformation requires understanding of development of wild fiber and developmental differences between wild and cultivated fiber.We examined early development of fibers, including abundance and placement on seed surface, nucleus position, presence of vacuoles, and fiber size and shape. Four species were studied using microscopic, morphometric, and statistical methods: Gossypium raimondii (wild D genome), Gossypium herbaceum (cultivated A genome), Gossypium hirsutum (wild tetraploid), and Gossypium hirsutum (cultivated tetraploid). Early fiber development is highly asynchronous in G ...


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 ...


Homeolog Specific Expression Bias, Ronald D. Smith 2016 College of William and Mary

Homeolog Specific Expression Bias, Ronald D. Smith

Biology and Medicine Through Mathematics Conference

No abstract provided.


Heterogeneous Responses To Viral Infection: Insights From Mathematical Modeling Of Yellow Fever Vaccine, James R. Moore 2016 Emory University

Heterogeneous Responses To Viral Infection: Insights From Mathematical Modeling Of Yellow Fever Vaccine, James R. Moore

Biology and Medicine Through Mathematics Conference

No abstract provided.


Facets: Allele-Specific Copy Number And Clonal Heterogeneity Analysis Tool Estimates For High-Throughput Dna Sequencing, Ronglai Shen, Venkatraman Seshan 2016 Memorial Sloan-Kettering Cancer Center

Facets: Allele-Specific Copy Number And Clonal Heterogeneity Analysis Tool Estimates For High-Throughput Dna Sequencing, Ronglai Shen, Venkatraman Seshan

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

Allele-specific copy number analysis (ASCN) from next generation sequenc- ing (NGS) data can greatly extend the utility of NGS beyond the iden- tification of mutations to precisely annotate the genome for the detection of homozygous/heterozygous deletions, copy-neutral loss-of-heterozygosity (LOH), allele-specific gains/amplifications. In addition, as targeted gene panels are increasingly used in clinical sequencing studies for the detection of “actionable” mutations and copy number alterations to guide treatment decisions, accurate, tumor purity-, ploidy-, and clonal heterogeneity-adjusted integer copy number calls are greatly needed to more reliably interpret NGS- based cancer gene copy number data in the context of clinical ...


Heart Failure Outcomes With Empagliflozin In Patients With Type 2 Diabetes At High Cardiovascular Risk: Results Of The Empa-Reg Outcome® Trial., David Fitchett, Bernard Zinman, Christoph Wanner, John M. Lachin, Stefan Hantel, Afshin Salsali, Odd Erik Johansen, Hans J Woerle, Uli C Broedl, Silvio E Inzucchi 2016 George Washington University

Heart Failure Outcomes With Empagliflozin In Patients With Type 2 Diabetes At High Cardiovascular Risk: Results Of The Empa-Reg Outcome® Trial., David Fitchett, Bernard Zinman, Christoph Wanner, John M. Lachin, Stefan Hantel, Afshin Salsali, Odd Erik Johansen, Hans J Woerle, Uli C Broedl, Silvio E Inzucchi

Epidemiology and Biostatistics Faculty Publications

AIMS: We previously reported that in the EMPA-REG OUTCOME(®) trial, empagliflozin added to standard of care reduced the risk of 3-point major adverse cardiovascular events, cardiovascular and all-cause death, and hospitalization for heart failure in patients with type 2 diabetes and high cardiovascular risk. We have now further investigated heart failure outcomes in all patients and in subgroups, including patients with or without baseline heart failure.

METHODS AND RESULTS: Patients were randomized to receive empagliflozin 10 mg, empagliflozin 25 mg, or placebo. Seven thousand and twenty patients were treated; 706 (10.1%) had heart failure at baseline. Heart failure hospitalization ...


Interpretable High-Dimensional Inference Via Score Maximization With An Application In Neuroimaging, Simon N. Vandekar, Philip T. Reiss, Russell T. Shinohara 2016 University of Pennsylvania

Interpretable High-Dimensional Inference Via Score Maximization With An Application In Neuroimaging, Simon N. Vandekar, Philip T. Reiss, Russell T. Shinohara

UPenn Biostatistics Working Papers

In the fields of neuroimaging and genetics a key goal is testing the association of a single outcome with a very high-dimensional imaging or genetic variable. Oftentimes summary measures of the high-dimensional variable are created to sequentially test and localize the association with the outcome. In some cases, the results for summary measures are significant, but subsequent tests used to localize differences are underpowered and do not identify regions associated with the outcome. We propose a generalization of Rao's score test based on maximizing the score statistic in a linear subspace of the parameter space. If the test rejects ...


A Link Between Paediatric Asthma And Obesity: Are They Caused By The Same Environmental Conditions?, Phylicia Gonsalves 2016 The University of Western Ontario

A Link Between Paediatric Asthma And Obesity: Are They Caused By The Same Environmental Conditions?, Phylicia Gonsalves

Electronic Thesis and Dissertation Repository

The highly associated paediatric conditions of asthma and overweight have seen dramatic increases over the past few decades. This thesis explored air pollution exposure as a potential underlying mechanism of co-morbid asthma and overweight among adolescents aged 12 to 18 years. Data from the Canadian Community Health Survey were merged with a database containing estimates of air pollution as assessed by particulate matter ≤ 2.5 microns (PM2.5) concentrations at the postal code centroid in southwestern Ontario. Logistic regression was used to conduct the analysis. Adolescents were more likely to be overweight as PM2.5 concentrations increased. There ...


Walking To Recovery - The Effects Of Postsurgical Ambulation On Patient Recovery Times, Trent William Stethen 2016 University of Tennessee, Knoxville

Walking To Recovery - The Effects Of Postsurgical Ambulation On Patient Recovery Times, Trent William Stethen

University of Tennessee Honors Thesis Projects

No abstract provided.


A Log Rank Test For Clustered Data Under Informative Within-Cluster Group Size., Mary Elizabeth Gregg 2016 University of Louisville

A Log Rank Test For Clustered Data Under Informative Within-Cluster Group Size., Mary Elizabeth Gregg

Electronic Theses and Dissertations

The log rank test is a popular nonparametric test for comparing the marginal survival distribution of two groups. When data are organized within clusters and the size of clusters or the distribution of group membership within a cluster is related to an outcome of interest, traditional methods of data analysis can be biased. In this thesis, we develop a within-cluster group weighted log rank test to compare marginal survival time distributions between groups from clustered data, correcting for cluster size and intra-cluster group size informativeness. The performance of this new test is compared with the unweighted and cluster-weighted log rank ...


Some Contributions To Nonparametric And Semiparametric Inference For Clustered And Multistate Data., Sandipan Dutta 2016 University of Louisville

Some Contributions To Nonparametric And Semiparametric Inference For Clustered And Multistate Data., Sandipan Dutta

Electronic Theses and Dissertations

This dissertation is composed of research projects that involve methods which can be broadly classified as either nonparametric or semiparametric. Chapter 1 provides an introduction of the problems addressed in these projects, a brief review of the related works that have done so far, and an outline of the methods developed in this dissertation. Chapter 2 describes in details the first project which aims at developing a rank-sum test for clustered data where an outcome from group in a cluster is associated with the number of observations belonging to that group in that cluster. Chapter 3 proposes the use of ...


Semi-Parametric Methods For Personalized Treatment Selection And Multi-State Models., Chathura K. Siriwardhana 2016 University of Louisville

Semi-Parametric Methods For Personalized Treatment Selection And Multi-State Models., Chathura K. Siriwardhana

Electronic Theses and Dissertations

This dissertation contains three research projects on personalized medicine and a project on multi-state modelling. The idea behind personalized medicine is selecting the best treatment that maximizes interested clinical outcomes of an individual based on his or her genetic and genomic information. We propose a method for treatment assignment based on individual covariate information for a patient. Our method covers more than two treatments and it can be applied with a broad set of models and it has very desirable large sample properties. An empirical study using simulations and a real data analysis show the applicability of the proposed procedure ...


Propensity Score Methods : A Simulation And Case Study Involving Breast Cancer Patients., John Craycroft 2016 University of Louisville

Propensity Score Methods : A Simulation And Case Study Involving Breast Cancer Patients., John Craycroft

Electronic Theses and Dissertations

Observational data presents unique challenges for analysis that are not encountered with experimental data resulting from carefully designed randomized controlled trials. Selection bias and unbalanced treatment assignments can obscure estimations of treatment effects, making the process of causal inference from observational data highly problematic. In 1983, Paul Rosenbaum and Donald Rubin formalized an approach for analyzing observational data that adjusts treatment effect estimates for the set of non-treatment variables that are measured at baseline. The propensity score is the conditional probability of assignment to a treatment group given the covariates. Using this score, one may balance the covariates across treatment ...


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