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Multi-group data, which include the same set of variables on separate groups of samples, are commonly seen in practice. Such data structure consists of data from multiple groups and can be challenging to analyze due to data heterogeneity. We propose a novel Joint and Individual Component Regression (JICO) model to analyze multi-group data. Our proposed model decomposes the response into shared and group-specific components, which are driven by low-rank approximations of joint and individual structures from the predictors respectively. The joint structure has the same regression coefficients across multiple groups, whereas individual structures have group-specific regression coefficients. We formulate this framework under the representation of latent components and propose an iterative algorithm to solve for the joint and individual scores. We us the Continuum Regression (CR) to estimate the latent scores, which provides a unified framework that covers the Ordinary Least Squares (OLS), the Partial Least Squares (PLS), and the Principal Component Regression (PCR) as its special cases. We show that JICO attains a good balance between global and group-specific models and remains flexible due to the usage of CR. We conduct simulation studies and analysis of an Alzheimer's disease dataset to further demonstrate the effectiveness of JICO. R package of JICO is available online at . for this article are available online. [ABSTRACT FROM AUTHOR]

Data are generated at an unprecedented rate and scale these days across many disciplines. The field of streaming data analysis has emerged as a result of new data collection and storage technologies in various areas, such as air pollution monitoring, detection of traffic congestion, disease surveillance, and recommendation systems. In this paper, we consider the problem of model estimation for data streams in reproducing kernel Hilbert spaces. We propose an adaptive supervised learning method with a data sparsity constraint that uses limited storage spaces and can handle nonstationary models. We demonstrate the competitive performance of the proposed method using simulations and analysis of the bike sharing dataset. [ABSTRACT FROM AUTHOR]

Objectives: To identify differences in practice patterns and outcomes related to the induction of general anesthesia for patients with pulmonary hypertension (PH) performed by anesthesiologists who have completed a cardiothoracic fellowship (CTA group) vs those who have not (non-CTA group). Design: Retrospective study with propensity score matching. Setting: Operating room. Participants: All adult patients with PH undergoing general anesthesia requiring intubation at a single academic center over 5 years. Interventions: Patient baseline characteristics, peri-induction management variables, post-induction mean arterial pressure (MAP), and other outcomes were compared between CTA and non-CTA groups. Methods and main results: Following propensity scoring matching, 402 patients were included in the final model, 100 in the CTA group and 302 in the non-CTA group. Also following matching, only cases of mild to moderate PH without right ventricular dysfunction remained in the analysis. Matched groups were overall statistically similar with respect to baseline characteristics; however, there was a greater incidence of higher ASA class (P =.025) and cardiology and thoracic procedures (P <.001) being managed by the CTA group. No statistical differences were identified in practice patterns or outcomes related to the induction of anesthesia between groups, except for longer hospital length of stay in the CTA group (P =.008). Conclusions: These results provide early evidence to suggest the induction of general anesthesia of patients with non-severe PH disease can be comparably managed by either anesthesiologists with or without a cardiothoracic fellowship. However, these findings should be confirmed in a prospective study. [ABSTRACT FROM AUTHOR]

Many biomedical studies collect data of mixed types of variables from multiple groups of subjects. Some of these studies aim to find the group‐specific and the common variation among all these variables. Even though similar problems have been studied by some previous works, their methods mainly rely on the Pearson correlation, which cannot handle mixed data. To address this issue, we propose a latent mixed Gaussian copula (LMGC) model that can quantify the correlations among binary, ordinal, continuous, and truncated variables in a unified framework. We also provide a tool to decompose the variation into the group‐specific and the common variation over multiple groups via solving a regularized M‐estimation problem. We conduct extensive simulation studies to show the advantage of our proposed method over the Pearson correlation‐based methods. We also demonstrate that by jointly solving the M‐estimation problem over multiple groups, our method is better than decomposing the variation group by group. We also apply our method to a Chlamydia trachomatis genital tract infection study to demonstrate how it can be used to discover informative biomarkers that differentiate patients. [ABSTRACT FROM AUTHOR]

Generalized linear models often have high-dimensional nuisance parameters, as seen in applications such as testing gene-environment interactions or gene-gene interactions. In these scenarios, it is essential to test the significance of a high-dimensional subvector of the model's coefficients. Although some existing methods can tackle this problem, they often rely on the bootstrap to approximate the asymptotic distribution of the test statistic, and are thus computationally expensive. Here, we propose a computationally efficient test with a closed-form limiting distribution, which allows the parameter being tested to be either sparse or dense. We show that, under certain regularity conditions, the Type-I error of the proposed method is asymptotically correct, and we establish its power under high-dimensional alternatives. Extensive simulations demonstrate the good performance of the proposed test and its robustness when certain sparsity assumptions are violated. We also apply the proposed method to Chinese famine sample data in order to show its performance when testing the significance of gene-environment interactions. [ABSTRACT FROM AUTHOR]

High‐dimensional data with censored outcomes of interest are prevalent in medical research. To analyze such data, the regularized Buckley–James estimator has been successfully applied to build accurate predictive models and conduct variable selection. In this paper, we consider the problem of parameter estimation and variable selection for the semiparametric accelerated failure time model for high‐dimensional block‐missing multimodal neuroimaging data with censored outcomes. We propose a penalized Buckley–James method that can simultaneously handle block‐wise missing covariates and censored outcomes. This method can also perform variable selection. The proposed method is evaluated by simulations and applied to a multimodal neuroimaging dataset and obtains meaningful results. [ABSTRACT FROM AUTHOR]

Multimodal data, where different types of data are collected from the same subjects, are fast emerging in a large variety of scientific applications. Factor analysis is commonly used in integrative analysis of multimodal data, and is particularly useful to overcome the curse of high dimensionality and high correlations. However, there is little work on statistical inference for factor analysis-based supervised modeling of multimodal data. In this article, we consider an integrative linear regression model that is built upon the latent factors extracted from multimodal data. We address three important questions: how to infer the significance of one data modality given the other modalities in the model; how to infer the significance of a combination of variables from one modality or across different modalities; and how to quantify the contribution, measured by the goodness of fit, of one data modality given the others. When answering each question, we explicitly characterize both the benefit and the extra cost of factor analysis. Those questions, to our knowledge, have not yet been addressed despite wide use of factor analysis in integrative multimodal analysis, and our proposal bridges an important gap. We study the empirical performance of our methods through simulations, and further illustrate with a multimodal neuroimaging analysis. [ABSTRACT FROM AUTHOR]

Oral immunotherapy (OIT) leads to suppression of mast cell and basophil degranulation along with changes in the adaptive immune response. This study aimed to determine how rapidly these effects occur during OIT and more broadly, the kinetics of basophil and mast cell suppression throughout the course of therapy. Twenty participants, age 4 to 12 years, were enrolled in a peanut OIT trial and assessed for desensitization and sustained unresponsiveness after 9 months of therapy. Blood was collected 5 times in the first month and then intermittently throughout to quantify immunoglobulins and assess basophil activation by CD63, CD203c, and phosphorylated SYK (pSYK). Twelve of 16 participants that completed the trial were desensitized after OIT, with 9 achieving sustained unresponsiveness after discontinuing OIT for 4 weeks. Basophil hyporesponsiveness, defined by lower CD63 expression, was detected as early as day 90. pSYK was correlated with CD63 expression, and there was a significant decrease in pSYK by day 250. CD203c expression remained unchanged throughout therapy. Interestingly, although basophil activation was decreased across the cohort during OIT, basophil activation did not correlate with individual clinical outcomes. Serum peanut-specific IgG 4 and IgA increased throughout therapy, whereas IgE remained unchanged. Suppression of basophil activation occurs within the first 90 days of peanut OIT, ultimately leading to suppression of signaling through pSYK. [ABSTRACT FROM AUTHOR]

When an infectious disease recurs, it may be due to treatment failure or a new infection. Being able to distinguish and classify these two different outcomes is critical in effective disease control. A multi-state model based on Markov processes is a typical approach to estimating the transition probability between the disease states. However, it can perform poorly when the disease state is unknown. This article aims to demonstrate that the transition likelihoods of baseline covariates can distinguish one cause from another with high accuracy in infectious diseases such as malaria. A more general model for disease progression can be constructed to allow for additional disease outcomes. We start from a multinomial logit model to estimate the disease transition probabilities and then utilize the baseline covariate's transition information to provide a more accurate classification result. We apply the expectation-maximization (EM) algorithm to estimate unknown parameters, including the marginal probabilities of disease outcomes. A simulation study comparing our classifier to the existing two-stage method shows that our classifier has better accuracy, especially when the sample size is small. The proposed method is applied to determining relapse vs reinfection outcomes in two Plasmodium vivax treatment studies from Cambodia that used different genotyping approaches to demonstrate its practical use. [ABSTRACT FROM AUTHOR]

Background: HIV‐infected individuals have a known increased risk of sudden cardiac death (SCD) compared to uninfected individuals. Implantable cardioverter‐defibrillators (ICDs) are standard therapy for preventing SCD; however, there is limited data on the outcomes of ICDs in HIV‐infected individuals. Hypothesis: HIV‐infected subjects receive a higher number of appropriate ICD therapies than uninfected controls. Methods: This is a retrospective cohort study of 35 consecutive HIV‐Infected patients and 36 uninfected controls matched by age, race, and gender who were treated at the University of North Carolina Medical Center in the outpatient or inpatient setting from 2014 to the present and had undergone ICD implantation. For HIV‐infected subjects, a multivariate Poisson regression analysis was performed to evaluate the association between covariates and ICD therapies. Results: Among HIV‐infected subjects, the mean CD4 count was 582.5 cells/mm3 and 69% had an undetectable viral load. The median follow‐up was 6.4 years. HIV‐infected subjects had both a higher number of appropriate ICD shocks or antitachycardia pacing (ATP) therapy per person‐year as well as a higher number of inappropriate ICD shocks per person‐year than uninfected controls (1.512 vs. 0.590 and 0.122 vs. 0.0166, respectively, p <.001 for both comparisons). After multivariate adjustment, the presence of detectable/unsuppressed viral load at the time of ICD implantation was an independent predictor of both of the following in HIV‐infected subjects: (1) appropriate ICD discharge (p =.004), and (2) appropriate ICD discharge or appropriate ATP therapy (p <.001). Conclusion: HIV‐infected subjects had a higher number of appropriate ICD discharge or ATP therapy per person‐year than matched uninfected controls. [ABSTRACT FROM AUTHOR]

Background: Mounting evidence shows that the neuropathological burdens manifest preference in affecting brain regions during the dynamic progression of Alzheimer's disease (AD). Since the distinct brain regions are physically wired by white matter fibers, it is reasonable to hypothesize the differential spreading pattern of neuropathological burdens may underlie the wiring topology, which can be characterized using neuroimaging and network science technologies.Objective: To study the dynamic spreading patterns of neuropathological events in AD.Methods: We first examine whether hub nodes with high connectivity in the brain network (assemble of white matter wirings) are susceptible to a higher level of pathological burdens than other regions that are less involved in the process of information exchange in the network. Moreover, we propose a novel linear mixed-effect model to characterize the multi-factorial spreading process of neuropathological burdens from hub nodes to non-hub nodes, where age, sex, and APOE4 indicators are considered as confounders. We apply our statistical model to the longitudinal neuroimaging data of amyloid-PET and tau-PET, respectively.Results: Our meta-data analysis results show that 1) AD differentially affects hub nodes with a significantly higher level of pathology, and 2) the longitudinal increase of neuropathological burdens on non-hub nodes is strongly correlated with the connectome distance to hub nodes rather than the spatial proximity.Conclusion: The spreading pathway of AD neuropathological burdens might start from hub regions and propagate through the white matter fibers in a prion-like manner. [ABSTRACT FROM AUTHOR]