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Stochastic Optimization Of Adaptive Enrichment Designs For Two Subpopulations, Aaron Fisher, Michael Rosenblum 2016 Harvard T.H. Chan School of Public Health

Stochastic Optimization Of Adaptive Enrichment Designs For Two Subpopulations, Aaron Fisher, Michael Rosenblum

Johns Hopkins University, Dept. of Biostatistics Working Papers

An adaptive enrichment design is a randomized trial that allows enrollment criteria to be modified at interim analyses, based on a preset decision rule. When there is prior uncertainty regarding treatment effect heterogeneity, these trial designs can provide improved power for detecting treatment effects in subpopulations. We present a simulated annealing approach to search over the space of decision rules and other parameters for an adaptive enrichment design. The goal is to minimize the expected number enrolled or expected duration, while preserving the appropriate power and Type I error rate. We also explore the benefits of parallel computation in the ...


An Examination Of The Neural Unreliability Thesis Of Autism, John Butler, Sophie Molholm, Gizely Andrade, John J. Foxe 2016 Dublin Institute of Technology

An Examination Of The Neural Unreliability Thesis Of Autism, John Butler, Sophie Molholm, Gizely Andrade, John J. Foxe

Articles

An emerging neuropathological theory of Autism, referred to here as “the neural unreliability thesis,” proposes greater variability in moment-to-moment cortical representation of environmental events, such that the system shows general instability in its impulse response function. Leading evidence for this thesis derives from functional neuroimaging, a methodology ill-suited for detailed assessment of sensory transmission dynamics occurring at the millisecond scale. Electrophysiological assessments of this thesis, however, are sparse and unconvincing. We conducted detailed examination of visual and somatosensory evoked activity using high-density electrical mapping in individuals with autism (N = 20) and precisely matched neurotypical controls (N = 20), recording large numbers ...


Pertussis-Associated Pneumonia In Infants And Children From Low- And Middle-Income Countries Participating In The Perch Study., Breanna Barger-Kamate, Maria Deloria Knoll, E Wangeci Kagucia, Christine Prosperi, Henry C Baggett, Daniel E. Park, +31 additional authors 2016 George Washington University

Pertussis-Associated Pneumonia In Infants And Children From Low- And Middle-Income Countries Participating In The Perch Study., Breanna Barger-Kamate, Maria Deloria Knoll, E Wangeci Kagucia, Christine Prosperi, Henry C Baggett, Daniel E. Park, +31 Additional Authors

Epidemiology and Biostatistics Faculty Publications

BACKGROUND:  Few data exist describing pertussis epidemiology among infants and children in low- and middle-income countries to guide preventive strategies.

METHODS:  Children 1-59 months of age hospitalized with World Health Organization-defined severe or very severe pneumonia in 7 African and Asian countries and similarly aged community controls were enrolled in the Pneumonia Etiology Research for Child Health study. They underwent a standardized clinical evaluation and provided nasopharyngeal and oropharyngeal swabs and induced sputum (cases only) for Bordetella pertussis polymerase chain reaction. Risk factors and pertussis-associated clinical findings were identified.

RESULTS:  Bordetella pertussis was detected in 53 of 4200 (1.3 ...


Gathering Steam In Health Care: A Student History, Michael J. Leach 2016 Bendigo Health

Gathering Steam In Health Care: A Student History, Michael J. Leach

The STEAM Journal

In this reflection, I demonstrate STEAM in health care by outlining my 15 years as a university student engaged in formal education, extracurricular learning, research, and employment.


Using Sensitivity Analyses For Unobserved Confounding To Address Covariate Measurement Error In Propensity Score Methods, Kara E. Rudolph, Elizabeth A. Stuart 2016 School of Public Health, University of California, Berkeley, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health

Using Sensitivity Analyses For Unobserved Confounding To Address Covariate Measurement Error In Propensity Score Methods, Kara E. Rudolph, Elizabeth A. Stuart

Johns Hopkins University, Dept. of Biostatistics Working Papers

Propensity score methods are a popular tool to control for confounding in observational data, but their bias-reduction properties are threatened by covariate measurement error. There are few easy-to-implement methods to correct for such bias. We describe and demonstrate how existing sensitivity analyses for unobserved confounding---propensity score calibration, Vanderweele and Arah's bias formulas, and Rosenbaum's sensitivity analysis---can be adapted to address this problem. In a simulation study, we examined the extent to which these sensitivity analyses can correct for several measurement error structures: classical, systematic differential, and heteroscedastic covariate measurement error. We then apply these approaches to address covariate ...


Confidence Intervals For Heritability Via Haseman-Elston Regression, Tamar Sofer 2016 University of Washington

Confidence Intervals For Heritability Via Haseman-Elston Regression, Tamar Sofer

UW Biostatistics Working Paper Series

Heritability is the proportion of phenotypic variance in a population that is attributable to individual genotypes. Heritability is considered an important measure in both evolutionary biology and in medicine, and is routinely estimated and reported in genetic epidemiology studies. In population-based genome-wide association studies (GWAS), mixed models are used to estimate variance components, from which a heritability estimate is obtained. The estimated heritability is the proportion of the model's total variance that is due to the genetic relatedness matrix (kinship measured from genotypes). Current practice is to use bootstrapping, which is slow, or normal asymptotic approximation to estimate the ...


Correlates Of Hiv Acquisition In A Cohort Of Black Men Who Have Sex With Men In The United States: Hiv Prevention Trials Network (Hptn) 061, Beryl A. Koblin, Kenneth H. Mayer, Susan H. Eshleman, Lei Wang, Sharon B. Mannheimer, Carlos Del Rio, Steve Shoptaw, Manya Magnus, Susan Buchbinder, Leo Wilton, Ting-Yuan Liu, Vanessa Cummings, Estelle Piwowar-Manning, Sheldon D. Fields, Sam Griffith, Vanessa Elharrar, Darrell Wheeler 2016 New York Blood Center, New York, NY

Correlates Of Hiv Acquisition In A Cohort Of Black Men Who Have Sex With Men In The United States: Hiv Prevention Trials Network (Hptn) 061, Beryl A. Koblin, Kenneth H. Mayer, Susan H. Eshleman, Lei Wang, Sharon B. Mannheimer, Carlos Del Rio, Steve Shoptaw, Manya Magnus, Susan Buchbinder, Leo Wilton, Ting-Yuan Liu, Vanessa Cummings, Estelle Piwowar-Manning, Sheldon D. Fields, Sam Griffith, Vanessa Elharrar, Darrell Wheeler

Leo Wilton

Background

Black men who have sex with men (MSM) in the United States (US) are affected by HIV at disproportionate rates compared to MSM of other race/ethnicities. Current HIV incidence estimates in this group are needed to appropriately target prevention efforts.

Methods

From July 2009 to October 2010, Black MSM reporting unprotected anal intercourse with a man in the past six months were enrolled and followed for one year in six US cities for a feasibility study of a multi-component intervention to reduce HIV infection. HIV incidence based on HIV seroconversion was calculated as number of events/100 person-years ...


Censoring Unbiased Regression Trees And Ensembles, Jon Arni Steingrimsson, Liqun Diao, Robert L. Strawderman 2016 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health

Censoring Unbiased Regression Trees And Ensembles, Jon Arni Steingrimsson, Liqun Diao, Robert L. Strawderman

Johns Hopkins University, Dept. of Biostatistics Working Papers

This paper proposes a novel approach to building regression trees and ensemble learning in survival analysis. By first extending the theory of censoring unbiased transformations, we construct observed data estimators of full data loss functions in cases where responses can be right censored. This theory is used to construct two specific classes of methods for building regression trees and regression ensembles that respectively make use of Buckley-James and doubly robust estimating equations for a given full data risk function. For the particular case of squared error loss, we further show how to implement these algorithms using existing software (e.g ...


Online Cross-Validation-Based Ensemble Learning, David Benkeser, Samuel D. Lendle, Cheng Ju, Mark J. van der Laan 2016 Division of Biostatistics, University of California, Berkeley

Online Cross-Validation-Based Ensemble Learning, David Benkeser, Samuel D. Lendle, Cheng Ju, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Online estimators update a current estimate with a new incoming batch of data without having to revisit past data thereby providing streaming estimates that are scalable to big data. We develop flexible, ensemble-based online estimators of an infinite-dimensional target parameter, such as a regression function, in the setting where data are generated sequentially by a common conditional data distribution given summary measures of the past. This setting encompasses a wide range of time-series models and as special case, models for independent and identically distributed data. Our estimator considers a large library of candidate online estimators and uses online cross-validation to ...


Doubly-Robust Nonparametric Inference On The Average Treatment Effect, David Benkeser, Marco Carone, Mark J. van der Laan, Peter Gilbert 2016 Division of Biostatistics, University of California, Berkeley

Doubly-Robust Nonparametric Inference On The Average Treatment Effect, David Benkeser, Marco Carone, Mark J. Van Der Laan, Peter Gilbert

U.C. Berkeley Division of Biostatistics Working Paper Series

Doubly-robust estimators are widely used to draw inference about the average effect of a treatment. Such estimators are consistent for the effect of interest if either one of two nuisance parameters is consistently estimated. However, if flexible, data-adaptive estimators of these nuisance parameters are used, double-robustness does not readily extend to inference. We present a general theoretical study of the behavior of doubly-robust estimators of an average treatment effect when one of the nuisance parameters is inconsistently estimated. We contrast different approaches for constructing such estimators and investigate the extent to which they may be modified to also allow doubly-robust ...


Comparative Effectiveness Research Using Observational Data: Active Comparators To Emulate Target Trials With Inactive Comparators, Anders Huitfeldt, Miguel A. Hernan, Mette Kalager, James M. Robins 2016 Harvard T.H. Chan School of Public Health

Comparative Effectiveness Research Using Observational Data: Active Comparators To Emulate Target Trials With Inactive Comparators, Anders Huitfeldt, Miguel A. Hernan, Mette Kalager, James M. Robins

eGEMs (Generating Evidence & Methods to improve patient outcomes)

Introduction: Because a comparison of non-initiators and initiators of treatment may be hopelessly confounded, guidelines for the conduct of observational research often recommend using an “active” comparator group consisting of people who initiate a treatment other than the medication of interest. In this paper, we discuss the conditions under which this approach is valid if the goal is to emulate a trial with an inactive comparator.

Identification of Effects: We provide conditions under which a target trial in a subpopulation can be validly emulated from observational data, using an active comparator that is known or believed to be inactive for ...


Performance-Constrained Binary Classification Using Ensemble Learning: An Application To Cost-Efficient Targeted Prep Strategies, Wenjing Zheng, Laura Balzer, Maya L. Petersen, Mark J. van der Laan 2016 Division of Biostatistics, School of Public Health, University of California, Berkeley

Performance-Constrained Binary Classification Using Ensemble Learning: An Application To Cost-Efficient Targeted Prep Strategies, Wenjing Zheng, Laura Balzer, Maya L. Petersen, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Binary classifications problems are ubiquitous in health and social science applications. In many cases, one wishes to balance two conflicting criteria for an optimal binary classifier. For instance, in resource-limited settings, an HIV prevention program based on offering Pre-Exposure Prophylaxis (PrEP) to select high-risk individuals must balance the sensitivity of the binary classifier in detecting future seroconverters (and hence offering them PrEP regimens) with the total number of PrEP regimens that is financially and logistically feasible for the program to deliver. In this article, we consider a general class of performance-constrained binary classification problems wherein the objective function and the ...


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

Johns Hopkins University, Dept. of Biostatistics Working Papers

Adjusting for prognostic baseline variables can lead to improved power in randomized trials. For binary outcomes, a logistic regression estimator is commonly used for such adjustment. This has resulted in substantial efficiency gains in practice, e.g., 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 following dangerous vulnerabilities: it is only interpretable ...


Pleiotropic Effects Of Csf Levels Of Alzheimer’S Disease Proteins, Olga A. Vsevolozhskaya, Ilai Keren, David W. Fardo, Dmitri V. Zaykin 2016 University of Kentucky

Pleiotropic Effects Of Csf Levels Of Alzheimer’S Disease Proteins, Olga A. Vsevolozhskaya, Ilai Keren, David W. Fardo, Dmitri V. Zaykin

Biostatistics Presentations

Cerebrospinal fluid (CSF) analytes harbor potential as diagnostic biomarkers for Alzheimer’s Disease (AD). Quantitative measures of CSF proteins comprise a set of often highly correlated endophenotypes that have previously shown promise in genetic analyses (Cruchaga et al., 2013; Kauwe et al., 2014). Pleiotropic impact of genetic variations on this set may provide additional insights into AD pathology at its earliest stages. To determine which specific endophenotypes are pleiotropic, one can employ methods based on the reverse regression of genotype on phenotypes. Recently, we proposed a method based functional linear models (Vsevolozhskaya et al, 2016) that utilizes reverse regression and ...


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.


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 ...


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