On the role of parameterization in models with a misspecified nuisance component.

The paper is concerned with inference for a parameter of interest in models that share a common interpretation for that parameter but that may differ appreciably in other respects. We study the general structure of models under which the maximum likelihood estimator of the parameter of interest is consistent under arbitrary misspecification of the nuisance part of the model. A specialization of the general results to matched-comparison and two-groups problems gives a more explicit and easily checkable condition in terms of a notion of symmetric parameterization, leading to a broadening and unification of existing results in those problems.

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 , 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 ( for testing ariance components).

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 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 ( for testing ariance components).

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.

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.

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

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.

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.