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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 ...


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 ...


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 ...


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 ...


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 ...


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 ...


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 ...


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