Spatial-extent inference for testing variance components in reliability and heritability studies.

Clusterwise inference is a popular approach in neuroimaging to increase sensitivity, but most existing methods are currently restricted to the General Linear Model (GLM) for testing mean parameters. Statistical methods for testing variance components, which are critical in neuroimaging studies that involve estimation of narrow-sense heritability or test-retest reliability, are underdeveloped due to methodological and computational challenges, which would potentially lead to low power. We propose a fast and powerful test for variance components called CLEAN-V (CLEAN for testing Variance components). CLEAN-V models the global spatial dependence structure of imaging data and computes a locally powerful variance component test statistic by data-adaptively pooling neighborhood information. Correction for multiple comparisons is achieved by permutations to control family-wise error rate (FWER). Through analysis of task-fMRI data from the Human Connectome Project across five tasks and comprehensive data-driven simulations, we show that CLEAN-V outperforms existing methods in detecting test-retest reliability and narrow-sense heritability with significantly improved power, with the detected areas aligning with activation maps. The computational efficiency of CLEAN-V also speaks of its practical utility, and it is available as an R package.

Baricitinib Versus Tocilizumab for the Treatment of Moderate to Severe COVID-19.

BACKGROUND: To date, minimal data directly compare tocilizumab with baricitinib for treatment in moderate to severe COVID-19. OBJECTIVE: To compare the rates of in-hospital mortality with progression to mechanical ventilation in patients with COVID-19 who received either tocilizumab or baricitinib. METHODS: The authors conducted a single-centered, institutional review board-approved, retrospective cohort study. Patients who were 18 years or older who were hospitalized with COVID-19 and who received tocilizumab or baricitinib were included. The primary end point is a composite outcome of progression to mechanical ventilation or in-hospital mortality. Secondary end points include components of the composite outcome and progression to higher level of care, duration of mechanical ventilation, and hospital and intensive care length of stay. Safety end points include the incidence of infections and thrombosis. RESULTS: A total of 176 patients were included, of whom 61 (34.7%) received tocilizumab and 115 (65.3%) received baricitinib. In the primary outcome, there was no difference between the groups (52.5% tocilizumab vs 44.3% baricitinib, P = 0.305). For safety outcomes, there was a higher instance of thrombosis (11.5% tocilizumab vs 3.5% baricitinib, P = 0.042) and rates of antibiotic use after initiation of therapy (55.7% tocilizumab vs 38.3% baricitinib, P = 0.026) in the tocilizumab group. CONCLUSION AND RELEVANCE: There was no significant difference in the composite outcome in patients who received tocilizumab or baricitinib for the treatment of COVID-19. However, there was an increase in rates of thrombosis in those receiving tocilizumab compared with baricitinib. These results need to be confirmed in larger prospective, randomized trials.

A New Class of Weighted CUSUM Statistics.

A change point is a location or time at which observations or data obey two different models: before and after. In real problems, we may know some prior information about the location of the change point, say at the right or left tail of the sequence. How does one incorporate the prior information into the current cumulative sum (CUSUM) statistics? We propose a new class of weighted CUSUM statistics with three different types of quadratic weights accounting for different prior positions of the change points. One interpretation of the weights is the mean duration in a random walk. Under the normal model with known variance, the exact distributions of these statistics are explicitly expressed in terms of eigenvalues. Theoretical results about the explicit difference of the distributions are valuable. The expansions of asymptotic distributions are compared with the expansion of the limit distributions of the Cramér-von Mises statistic and the Anderson and Darling statistic. We provide some extensions from independent normal responses to more interesting models, such as graphical models, the mixture of normals, Poisson, and weakly dependent models. Simulations suggest that the proposed test statistics have better power than the graph-based statistics. We illustrate their application to a detection problem with video data.

Long-term effects of medication for opioid use disorder in children.

ABSTRACT: Opioid maintenance therapy in pregnant patients can result in children born with neonatal abstinence syndrome (NAS). These infants are at high risk for poor school performance, unemployment, and criminal activity because they never reach the neurocognitive levels of their peers. This article discusses the neurocognitive development consequences of medicated opioid use disorder on infants and children and methods to help them reach their potential into adulthood.

On specification tests for composite likelihood inference.

Composite likelihood functions are often used for inference in applications where the data have a complex structure. While inference based on the composite likelihood can be more robust than inference based on the full likelihood, the inference is not valid if the associated conditional or marginal models are misspecified. In this paper, we propose a general class of specification tests for composite likelihood inference. The test statistics are motivated by the fact that the second Bartlett identity holds for each component of the composite likelihood function when these components are correctly specified. We construct the test statistics based on the discrepancy between the so-called composite information matrix and the sensitivity matrix. As an illustration, we study three important cases of the proposed tests and establish their limiting distributions under both null and local alternative hypotheses. Finally, we evaluate the finite-sample performance of the proposed tests in several examples.

Molecular classification of oral cancer by cDNA microarrays identifies overexpressed genes correlated with nodal metastasis.

Our purpose was to classify OSCCs based on their gene expression profiles, to identify differentially expressed genes in these cancers and to correlate genetic deregulation with clinical and histopathologic data and patient outcome. After conducting proof-of-principle experiments utilizing 6 HNSCC cell lines, the gene expression profiles of 20 OSCCs were determined using cDNA microarrays containing 19,200 sequences and the BTSVQ method of data analysis. We identified 2 sample clusters that correlated with the T3-T4 category of disease (p = 0.035) and nodal metastasis (p = 0.035). BTSVQ analysis identified a subset of 23 differentially expressed genes with the lowest QE scores in the cluster containing more advanced-stage tumors. Expression of 6 of these differentially expressed genes was validated by quantitative real-time RT-PCR. Statistical analysis of quantitative real-time RT-PCR data was performed and, after Bonferroni correction, CLDN1 overexpression was significantly correlated with the cluster containing more advanced-stage tumors (p = 0.007). Despite the clinical heterogeneity of OSCC, molecular subtyping by cDNA microarray analysis identified distinct patterns of gene expression associated with relevant clinical parameters. Application of this methodology represents an advance in the classification of oral cavity tumors and may ultimately aid in the development of more tailored therapies for oral carcinoma.