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Background: The U.S. uninsured rate has dropped significantly since the passage of the Affordable Care Act (ACA), yet insurance coverage remains lower in historically marginalized communities than in the overall population. New consumer engagement approaches that involve these populations are needed. The purpose of this study was to evaluate the effectiveness of the Insuring Good Health Intervention, a website and video series designed to improve engagement with health insurance and ACA reforms. The study was designed and implemented using a community-based participatory research approach. Method: We conducted a lagged-control cluster randomized controlled trial to evaluate Insuring Good Health with racially and ethnically diverse adults seeking services in medically underserved areas within the Detroit, Michigan metropolitan area. Outcomes were assessed at baseline and at 6 and 9 months postintervention, and including self-efficacy, knowledge, beliefs, and intention to seek help with insurance navigation and care. Results: Among 243 participants, mean age was 43.4 (SD = 13) years, and all participants met federal guidelines for poverty. The study had an 86% response rate at 9-month follow-up (n = 209). Compared with the lagged-control group, intervention participants had more positive beliefs concerning preventive care (estimate 0.51, standard error 0.16; p < .01), and intention to seek help with insurance navigation and care (estimate 0.43, standard error 0.17; p < .001) at 9-month follow-up. Hispanic participants benefitted the most from the intervention, including improved knowledge of health insurance eligibility over 9 months compared with other racial/ethnic groups (estimate -0.97, standard error 0.40, p < .01). Discussion: Insuring Good Health was associated with increased intention to seek help with insurance navigation and care, and improved beliefs around preventive care. Conclusions: Insuring Good Health has the potential to be an important consumer engagement tool to reduce disparities in health insurance coverage and care seeking.

Data sharing barriers present paramount challenges arising from multicenter clinical studies where multiple data sources are stored and managed in a distributed fashion at different local study sites. Merging such data sources into a common data storage for a centralized statistical analysis requires a data use agreement, which is often time‐consuming. Data merging may become more burdensome when propensity score modeling is involved in the analysis because combining many confounding variables, and systematic incorporation of this additional modeling in a meta‐analysis has not been thoroughly investigated in the literature. Motivated from a multicenter clinical trial of basal insulin treatment for reducing the risk of post‐transplantation diabetes mellitus, we propose a new inference framework that avoids the merging of subject‐level raw data from multiple sites at a centralized facility but needs only the sharing of summary statistics. Unlike the architecture of federated learning, the proposed collaborative inference does not need a center site to combine local results and thus enjoys maximal protection of data privacy and minimal sensitivity to unbalanced data distributions across data sources. We show theoretically and numerically that the new distributed inference approach has little loss of statistical power compared to the centralized method that requires merging the entire data. We present large‐sample properties and algorithms for the proposed method. We illustrate its performance by simulation experiments and the motivating example on the differential average treatment effect of basal insulin to lower risk of diabetes among kidney‐transplant patients compared to the standard‐of‐care. [ABSTRACT FROM AUTHOR]

Mediation analysis aims to assess if, and how, a certain exposure influences an outcome of interest through intermediate variables. This problem has recently gained a surge of attention due to the tremendous need for such analyses in scientific fields. Testing for the mediation effect (ME) is greatly challenged by the fact that the underlying null hypothesis (i.e. the absence of MEs) is composite. Most existing mediation tests are overly conservative and thus underpowered. To overcome this significant methodological hurdle, we develop an adaptive bootstrap testing framework that can accommodate different types of composite null hypotheses in the mediation pathway analysis. Applied to the product of coefficients test and the joint significance test, our adaptive testing procedures provide type I error control under the composite null, resulting in much improved statistical power compared to existing tests. Both theoretical properties and numerical examples of the proposed methodology are discussed. [ABSTRACT FROM AUTHOR]

It is important to develop statistical techniques to analyze high-dimensional data in the presence of both complex dependence and possible heavy tails and outliers in real-world applications such as imaging data analyses. We propose a new robust high-dimensional regression with coefficient thresholding, in which an efficient nonconvex estimation procedure is proposed through a thresholding function and the robust Huber loss. The proposed regularization method accounts for complex dependence structures in predictors and is robust against heavy tails and outliers in outcomes. Theoretically, we rigorously analyze the landscape of the population and empirical risk functions for the proposed method. The fine landscape enables us to establish both statistical consistency and computational convergence under the high-dimensional setting. We also present an extension to incorporate spatial information into the proposed method. Finite-sample properties of the proposed methods are examined by extensive simulation studies. An application concerns a scalar-on-image regression analysis for an association of psychiatric disorder measured by the general factor of psychopathology with features extracted from the task functional MRI data in the Adolescent Brain Cognitive Development (ABCD) study. for this article are available online. [ABSTRACT FROM AUTHOR]

This article explores the reliability of statistical inference for streaming longitudinal data. The authors argue that previous research's assumption of no abnormal data cases is unrealistic and propose a simulation experiment to evaluate their proposed method's performance in the presence of data contamination. The results indicate that the method is robust against mild contamination but exhibits biases and discrepancies with high contamination. The authors suggest further investigation into the weighting function used to achieve robustness. They conclude that the method is resilient against mild contamination and has potential for practical applications. The authors also acknowledge their funding support. [Extracted from the article]

Mediation analysis using structural equation models has become a widely used tool to study whether the effect of an exposure on an outcome is mediated by some intermediate factors. When multiple mediators are present, a statistical inference on the joint mediation effect is challenging because of the composite null hypotheses with a large number of parameter configurations. We propose a simultaneous likelihood ratio test that uses a block coordinate descent algorithm to solve the constrained likelihood under the irregular null parameter space using the Lagrange multiplier approach. We establish the asymptotic null distribution and examine the performance of the proposed joint test statistic using extensive simulations and a comparison with existing tests. The simulation results show that our method controls the type I error properly and, in general, provides better power than that of existing test methods. We apply our method to examine whether a group of glucose metabolites and acetylamino acids mediate the effect of nutrient intakes on insulin resistance. [ABSTRACT FROM AUTHOR]

This article develops an incremental learning algorithm based on quadratic inference function (QIF) to analyze streaming datasets with correlated outcomes such as longitudinal data and clustered data. We propose a renewable QIF (RenewQIF) method within a paradigm of renewable estimation and incremental inference, in which parameter estimates are recursively renewed with current data and summary statistics of historical data, but with no use of any historical subject-level raw data. We compare our renewable estimation method with both offline QIF and offline generalized estimating equations (GEE) approach that process the entire cumulative subject-level data all together, and show theoretically and numerically that our renewable procedure enjoys statistical and computational efficiency. We also propose an approach to diagnose the homogeneity assumption of regression coefficients via a sequential goodness-of-fit test as a screening procedure on occurrences of abnormal data batches. We implement the proposed methodology by expanding existing Spark's Lambda architecture for the operation of statistical inference and data quality diagnosis. We illustrate the proposed methodology by extensive simulation studies and an analysis of streaming car crash datasets from the National Automotive Sampling System-Crashworthiness Data System (NASS CDS). for this article are available online. [ABSTRACT FROM AUTHOR]

Rationale: Asthma is a variable condition with an apparent tendency for a natural decline in asthma symptoms and health care use occurring as children age. As a result, asthma interventions using a pre-post design may overestimate the intervention effect when no proper control group is available. Objectives: Investigate patterns of natural decline over time with increasing age in asthma symptoms and health care use of children. Develop a statistical procedure that enables adjustment that accounts for expected declines in these outcomes and is useable when intervention evaluations must rely solely on pre-post data. Methods: Mixed-effects models with mixture distributions were used to describe the pattern of symptoms and health care use in 3,021 children aged 2 to 15 years in a combined sample from three controlled trials. An adaptive least squares estimation was used to account for overestimation of intervention effects and make adjustments for pre-post only data. Termed "Adjustment for Natural Declines in Asthma Outcomes (ANDAO)," the adjustment method uses bootstrap sampling to create control cohorts comparable to subjects in the intervention study from existing control subjects. ANDAO accounts for expected declines in outcomes and is beneficial when intervention evaluations must rely solely on pre-post data. Measurements and Main Results. Children under 10 years of age experienced 18% (95% confidence interval, 15-21%) fewer symptom days and 28% (95% confidence interval, 24-32%) fewer symptom nights with each additional year of age. The decline was less than 10% after age 10 years, depending on baseline asthma severity. Emergency department visits declined regardless of baseline symptom frequency (P = 0.02). The adjustment method corrected estimates to within 2.4% of true effects through simulations using control cohorts. Conclusions. Because of the declines in symptoms and health care use expected with increasing age of children with asthma, pre-post comparisons will greatly overestimate intervention effects. The ANDAO provides means to adequately estimate treatment effects when a control group design is not possible.

Longitudinal outcomes are prevalent in clinical studies, where the presence of missing data may make the statistical learning of individualized treatment rules (ITRs) a much more challenging task. We analyzed a longitudinal calcium supplementation trial in the ELEMENT Project and established a novel ITR to reduce the risk of adverse outcomes of lead exposure on child growth and development. Lead exposure, particularly in the form of in utero exposure, can seriously impair children's health, especially their cognitive and neurobehavioral development, which necessitates clinical interventions such as calcium supplementation intake during pregnancy. Using the longitudinal outcomes from a randomized clinical trial of calcium supplementation, we developed a new ITR for daily calcium intake during pregnancy to mitigate persistent lead exposure in children at age 3 years. To overcome the technical challenges posed by missing data, we illustrate a new learning approach, termed longitudinal self‐learning (LS‐learning), that utilizes longitudinal measurements of child's blood lead concentration in the derivation of ITR. Our LS‐learning method relies on a temporally weighted self‐learning paradigm to synergize serially correlated training data sources. The resulting ITR is the first of this kind in precision nutrition that will contribute to the reduction of expected blood lead concentration in children aged 0‐3 years should this ITR be implemented to the entire study population of pregnant women. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed empirical likelihood (DEL) method for performing an integrative analysis of multiple data sources with the flexibility of handling either homogeneous or heterogeneous data. The proposed DEL method does not require pooling individual data sets into a centralized operational platform, so the privacy of subject-level information in individual data sources is protected. The DEL method is shown to be almost surely equal to the centralized empirical likelihood approach that would be adopted if individual data sets were combined and stored at one place. We establish the large-sample properties and algorithm convergence of the DEL method. We also illustrate the numerical performance of the DEL method using simulation studies and a real-data example, in which the DEL method is clearly advantageous over the classical meta-estimation method when analyzing unbalanced data sets. [ABSTRACT FROM AUTHOR]

New data collection and storage technologies have given rise to a new field of streaming data analytics, called real‐time statistical methodology for online data analyses. Most existing online learning methods are based on homogeneity assumptions, which require the samples in a sequence to be independent and identically distributed. However, inter‐data batch correlation and dynamically evolving batch‐specific effects are among the key defining features of real‐world streaming data such as electronic health records and mobile health data. This article is built under a state‐space mixed model framework in which the observed data stream is driven by a latent state process that follows a Markov process. In this setting, online maximum likelihood estimation is made challenging by high‐dimensional integrals and complex covariance structures. In this article, we develop a real‐time Kalman‐filter‐based regression analysis method that updates both point estimates and their standard errors for fixed population average effects while adjusting for dynamic hidden effects. Both theoretical justification and numerical experiments demonstrate that our proposed online method has statistical properties similar to those of its offline counterpart and enjoys great computational efficiency. We also apply this method to analyze an electronic health record dataset. [ABSTRACT FROM AUTHOR]