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A Synthesis Of Current Surveillance Planning Methods For The Sequential Monitoring Of Drug And Vaccine Adverse Effects Using Electronic Health Care Data, Jennifer C. Nelson, Robert Wellman, Onchee Yu, Andrea J. Cook, Judith C. Maro, Rita Ouellet-Hellstrom, Denise Boudreau, James S. Floyd, Susan R. Heckbert, Simone Pinheiro, Marsha Reichman, Azadeh Shoaibi 2016 Group Health Research Institute; University of Washington

A Synthesis Of Current Surveillance Planning Methods For The Sequential Monitoring Of Drug And Vaccine Adverse Effects Using Electronic Health Care Data, Jennifer C. Nelson, Robert Wellman, Onchee Yu, Andrea J. Cook, Judith C. Maro, Rita Ouellet-Hellstrom, Denise Boudreau, James S. Floyd, Susan R. Heckbert, Simone Pinheiro, Marsha Reichman, Azadeh Shoaibi

eGEMs (Generating Evidence & Methods to improve patient outcomes)

Introduction: The large-scale assembly of electronic health care data combined with the use of sequential monitoring has made proactive postmarket drug- and vaccine-safety surveillance possible. Although sequential designs have been used extensively in randomized trials, less attention has been given to methods for applying them in observational electronic health care database settings.

Existing Methods: We review current sequential-surveillance planning methods from randomized trials, and the Vaccine Safety Datalink (VSD) and Mini-Sentinel Pilot projects—two national observational electronic health care database safety monitoring programs.

Future Surveillance Planning: Based on this examination, we suggest three steps for future surveillance planning in health ...


Model Averaged Double Robust Estimation, Matthew Cefalu, Francesca Dominici, Nils D. Arvold MD, Giovanni Parmigiani 2016 Harvard School of Public Health

Model Averaged Double Robust Estimation, Matthew Cefalu, Francesca Dominici, Nils D. Arvold Md, Giovanni Parmigiani

Harvard University Biostatistics Working Paper Series

Existing methods in causal inference do not account for the uncertainty in the selection of confounders. We propose a new class of estimators for the average causal effect, the model averaged double robust estimators, that formally account for model uncertainty in both the propensity score and outcome model through the use of Bayesian model averaging. These estimators build on the desirable double robustness property by only requiring the true propensity score model or the true outcome model be within a specified class of models to maintain consistency. We provide asymptotic results and conduct a large scale simulation study that indicates ...


Prevalence Estimation At The Cluster Level For Correlated Binary Data Using Random Partial-Cluster Sampling, Rujin Wang, John S. Preisser 2016 University of North Carolina at Chapel Hill

Prevalence Estimation At The Cluster Level For Correlated Binary Data Using Random Partial-Cluster Sampling, Rujin Wang, John S. Preisser

The University of North Carolina at Chapel Hill Department of Biostatistics Technical Report Series

For clustered data in the medical sciences, disease is present when one or more of the observations in the cluster has the disease condition. This paper focuses on estimation of periodontal disease prevalence defined as the probability that one or more tooth sites have disease in a randomly selected subject. The prohibitive exam time and monetary cost of the full-mouth examination makes partial-mouth recording protocols attractive alternative methods to assess chronic periodontitis. In particular, Beck et al. (2006) proposed the random site selection method (RSSM), which pre-specifies a fixed number of tooth sites to be selected randomly from each subject ...


Distance-Based Analysis Of Variance For Brain Connectivity, Russell T. Shinohara, Haochang Shou, Marco Carone, Robert Schultz, Birkan Tunc, Drew Parker, Ragini Verma 2016 Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania

Distance-Based Analysis Of Variance For Brain Connectivity, Russell T. Shinohara, Haochang Shou, Marco Carone, Robert Schultz, Birkan Tunc, Drew Parker, Ragini Verma

UPenn Biostatistics Working Papers

The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using complex structures including matrices, functions, and graphs, which require specialized statistical techniques for estimation and inference about developmental and disorder-related changes. Unfortunately, classical statistical testing procedures are not well suited to high-dimensional testing problems. In the context of global or regional tests for differences in neuroimaging data, traditional analysis of variance (ANOVA) is not directly applicable without first summarizing the data into univariate or low-dimensional features, a process that may ...


Addition To Pglr Chap 6, Joseph M. Hilbe 2016 Arizona State University

Addition To Pglr Chap 6, Joseph M. Hilbe

Joseph M Hilbe

Addition to Chapter 6 in Practical Guide to Logistic Regression. Added section on Bayesian logistic regression using Stata.


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 ...


The Use Of Permutation Tests For The Analysis Of Parallel And Stepped-Wedge Cluster Randomized Trials, Rui Wang, Victor DeGruttola 2016 Harvard University

The Use Of Permutation Tests For The Analysis Of Parallel And Stepped-Wedge Cluster Randomized Trials, Rui Wang, Victor Degruttola

Harvard University Biostatistics Working Paper Series

We investigate the use of permutation tests for the analysis of parallel and stepped-wedge cluster randomized trials. Permutation tests for parallel designs with exponential family endpoints have been extensively studied. The optimal permutation tests developed for exponential family alternatives require information on intraclass correlation, a quantity not yet defined for time-to-event endpoints. Therefore, it is unclear how efficient permutation tests can be constructed for cluster-randomized trials with such endpoints. We consider a class of test statistics formed by a weighted average of pair-specific treatment effect estimates and offer practical guidance on the choice of weights to improve efficiency. We apply ...


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 ...


An Activity Index For Raw Accelerometry Data And Its Comparison With Other Activity Metrics, J Bai, C Z. Di, L Xiao, K R. Evenson, A Z. LaCroix, C M. Crainiceanu, D M. Buchner 2016 Selected Works

An Activity Index For Raw Accelerometry Data And Its Comparison With Other Activity Metrics, J Bai, C Z. Di, L Xiao, K R. Evenson, A Z. Lacroix, C M. Crainiceanu, D M. Buchner

Chongzhi Di

Accelerometers have been widely deployed in public health studies in recent years. While they collect high-resolution acceleration signals (e.g., 10-100 Hz), research has mainly focused on summarized metrics provided by accelerometers manufactures, such as the activity count (AC) by ActiGraph or Actical. Such measures do not have a publicly available formula, lack a straightforward interpretation, and can vary by software implementation or hardware type. To address these problems, we propose the physical activity index (AI), a new metric for summarizing raw tri-axial accelerometry data. We compared this metric with the AC and another recently proposed metric for raw data ...


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 ...


Mediation Analysis For A Survival Outcome With Time-Varying Exposures, Mediators, And Confounders, Sheng-Hsuan Lin, Jessica G. Young, Roger Logan, Tyler J. VanderWeele 2016 Department of Biostatistics, Columbia Mailman School of Public Health

Mediation Analysis For A Survival Outcome With Time-Varying Exposures, Mediators, And Confounders, Sheng-Hsuan Lin, Jessica G. Young, Roger Logan, Tyler J. Vanderweele

Harvard University Biostatistics Working Paper Series

We propose an approach to conduct mediation analysis for survival data with time-varying exposures, mediators, and confounders. We identify certain interventional direct and indirect effects through a survival mediational g-formula and describe the required assumptions. We also provide a feasible parametric approach along with an algorithm and software to estimate these effects. We apply this method to analyze the Framingham Heart Study data to investigate the causal mechanism of smoking on mortality through coronary artery disease. The risk ratio of smoking 30 cigarettes per day for ten years compared with no smoking on mortality is 2.34 (95 % CI = (1 ...


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 ...


Propensity Score Based Methods For Estimating The Treatment Effects Based On Observational Studies., Younathan Abdia 2016 University of Louisville

Propensity Score Based Methods For Estimating The Treatment Effects Based On Observational Studies., Younathan Abdia

Electronic Theses and Dissertations

This dissertation consists of two interconnected research projects. The first project was a study of propensity scores based statistical methods for estimating the average treatment effect (ATE) and the average treatment effect among treated (ATT) when there are two treatment groups. The ATE is defined as the mean of the individual causal effects in the whole population, while ATT is defined as the treatment effect for the treated population. Propensity score based statistical methods, such as matching, regression, stratification, inverse probability weighting (IPW), and doubly robust (DR) methods were used to estimate the ATE and ATT. Simulation studies and case ...


Variable Selection Via Penalized Regression And The Genetic Algorithm Using Information Complexity, With Applications For High-Dimensional -Omics Data, Tyler J. Massaro 2016 University of Tennessee, Knoxville

Variable Selection Via Penalized Regression And The Genetic Algorithm Using Information Complexity, With Applications For High-Dimensional -Omics Data, Tyler J. Massaro

Doctoral Dissertations

This dissertation is a collection of examples, algorithms, and techniques for researchers interested in selecting influential variables from statistical regression models. Chapters 1, 2, and 3 provide background information that will be used throughout the remaining chapters, on topics including but not limited to information complexity, model selection, covariance estimation, stepwise variable selection, penalized regression, and especially the genetic algorithm (GA) approach to variable subsetting.

In chapter 4, we fully develop the framework for performing GA subset selection in logistic regression models. We present advantages of this approach against stepwise and elastic net regularized regression in selecting variables from a ...


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 ...


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