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National and State Estimates of Adults with Autism Spectrum Disorder
U.S. national and state population-based estimates of adults living with autism spectrum disorder (ASD) are nonexistent due to the lack of existing surveillance systems funded to address this need. Therefore, we estimated national and state prevalence of adults 18–84 years living with ASD using simulation in conjunction with Bayesian hierarchal models. In 2017, we estimated that approximately 2.21% (95% simulation interval (SI) 1.95%, 2.45%) or 5,437,988 U.S. adults aged 18 and older have ASD, with state prevalence ranging from 1.97% (95% SI 1.55%, 2.45%) in Louisiana to 2.42% (95% SI 1.93%, 2.99%) in Massachusetts. Prevalence and case estimates of adults living with ASD (diagnosed and undiagnosed) can help states estimate the need for diagnosing and providing services to those unidentified.
Journal Article
Detecting Novel Associations in Large Data Sets
Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we present a measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination (R²) of the data relative to the regression function. MIC belongs to a larger class of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships. We apply MIC and MINE to data sets in global health, gene expression, major-league baseball, and the human gut microbiota and identify known and novel relationships.
Journal Article
Handbook of regression analysis
\"Written by two established experts in the field, the purpose of this handbook is to provide a practical, one-stop reference on regression analysis. The focus is on the tools that both practitioners and researchers use in real life. It is intended to be a comprehensive collection of the theory, methods, and applications of the subject matter, but it is deliberately written at an accessible level. The handbook will provide a quick and convenient reference or \"refresher\" on ideas and methods that are useful for the accurate analysis of data and its resulting interpretations. Students can use the book as an introduction to and/or summary of key concepts in regression and related course work (such as linear, nonlinear, and nonparametric regressions). Plentiful references are supplied for the more motivated readers. Theory is presented when necessary, and always supplemented by hands-on examples. Software routines are available via an author-maintained web site\"-- Provided by publisher.
Book
Meta-analysis with Robust Variance Estimation: Expanding the Range of Working Models
In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to include all dependent effect sizes in a single meta-regression model, even when the exact form of the dependence is unknown. RVE uses a working model of the dependence structure, but the two currently available working models are limited to each describing a single type of dependence. Drawing on flexible tools from multilevel and multivariate meta-analysis, this paper describes an expanded range of working models, along with accompanying estimation methods, which offer potential benefits in terms of better capturing the types of data structures that occur in practice and, under some circumstances, improving the efficiency of meta-regression estimates. We describe how the methods can be implemented using existing software (the “metafor” and “clubSandwich” packages for R), illustrate the proposed approach in a meta-analysis of randomized trials on the effects of brief alcohol interventions for adolescents and young adults, and report findings from a simulation study evaluating the performance of the new methods.
Journal Article
Deep Learning With TensorFlow: A Review
This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models also has tremendous potential to promote data analysis and modeling for various problems in educational and behavioral sciences given its flexibility and scalability. We give the reader an overview of the basics of neural network models such as the multilayer perceptron, the convolutional neural network, and stochastic gradient descent, the most commonly used optimization method for neural network models. However, the implementation of these models and optimization algorithms is time-consuming and error-prone. Fortunately, TensorFlow greatly eases and accelerates the research and application of neural network models. We review several core concepts of TensorFlow such as graph construction functions, graph execution tools, and TensorFlow’s visualization tool, TensorBoard. Then, we apply these concepts to build and train a convolutional neural network model to classify handwritten digits. This review is concluded by a comparison of low- and high-level application programming interfaces and a discussion of graphical processing unit support, distributed training, and probabilistic modeling with TensorFlow Probability library.
Journal Article
Effectiveness of nirsevimab immunoprophylaxis against respiratory syncytial virus-related outcomes in hospital and primary care settings: a retrospective cohort study in infants in Catalonia (Spain)
BackgroundIn Catalonia, infants under 6 months old were eligible to receive nirsevimab, a novel monoclonal antibody against respiratory syncytial virus (RSV). We aimed to analyse nirsevimab’s effectiveness across primary and hospital care outcomes.MethodsRetrospective cohort study from 1 October 2023 to 31 January 2024, including all infants born between April and September 2023. We established two cohorts based on nirsevimab administration (immunised and non-immunised). We followed individuals until the earliest moment of an outcome—RSV infection, primary care attended bronchiolitis and pneumonia, hospital emergency visits due to bronchiolitis, hospital admission or intensive care unit (ICU) admission due to RSV bronchiolitis—death or the end of the study. We used the Kaplan-Meier estimator and fitted Cox regression models using a calendar time scale to estimate HRs and their 95% CIs.ResultsAmong 26 525 infants, a dose of nirsevimab led to an adjusted HR for hospital admission due to RSV bronchiolitis of 0.124 (95% CI: 0.086 to 0.179) and an adjusted HR for ICU admission of 0.099 (95% CI: 0.041 to 0.237). Additionally, the adjusted HRs observed for emergency visits were 0.446 (95% CI: 0.385 to 0.516) and 0.393 (95% CI: 0.203 to 0.758) for viral pneumonia, 0.519 (95% CI: 0.467 to 0.576) for bronchiolitis attended in primary care and 0.311 (95% CI: 0.200 to 0.483) for RSV infection.ConclusionWe demonstrated nirsevimab’s effectiveness with reductions of 87.6% and 90.1% in hospital and ICU admissions, respectively. These findings offer crucial guidance for public health authorities in implementing RSV immunisation campaigns.
Journal Article