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Gender norms have been posited to impact intimate partner violence (IPV), but there is scant evidence of the longitudinal association between community-level gender norms and IPV. Using longitudinal data on 3,965 married girls surveyed in India, we fitted mixed-effects ordinal and binary logistic regression models for physical IPV intensity and occurrence of sexual IPV. We found a 26% increase in the odds that women experience frequent physical IPV per one unit increase in greater community-level equitable gender norms. We did not find an association between community-level equitable gender norms and sexual IPV. Findings suggest that the relationship between gender norms and physical and sexual IPV differs, indicating the need for tailored interventions for different types of IPV.

PurposeOur goal was to identify new anticancer agents approved by the US Food and Drug Administration (FDA) and the European Medical Agency (EMA) since the 2016 MASCC/ESMO antiemetic update and classify their emetic potential.MethodsThe MASCC/ESMO Expert Panel classified the emetogenicity of the identified new antineoplastic agents based on nonsystematic reviews of randomized controlled trials, analysis of product labeling, and evaluation of emetic classification in other international guidelines and informal consensus. The emetogenic classification system for oral anticancer agents was revised into two emetic risk categories (minimal-low; moderate-high) to be consistent with the system reported by ASCO (American Society of Clinical Oncology) in their 2017 guideline update. The previously employed four emetic risk classification categories for intravenously administered antineoplastic agents were retained for this update.ResultsFrom June 2015 to January 2023, 107 new antineoplastic agents (44 intravenously administered and 63 orally administered agents) were identified. The reported incidence of vomiting varied significantly across studies for many agents, especially for oral anticancer agents.ConclusionThe MASCC/ESMO Expert Panel acknowledges the limitations of our efforts to classify the emetic potential of anticancer agents, especially the imprecision associated with oral agents. However, we have attempted to provide a reasonable approximation of the emetic risk associated with new antineoplastic agents by searching the available literature and reviewing other available international antiemetic guidelines.

PURPOSE: This systematic review updates the MASCC/ESMO recommendations for high-emetic-risk chemotherapy (HEC) published in 2016-2017. HEC still includes cisplatin, carmustine, dacarbazine, mechlorethamine, streptozocin, and cyclophosphamide in doses of > 1500 mg/m2 and the combination of cyclophosphamide and an anthracycline (AC) in women with breast cancer. METHODS: A systematic review report following the PRISMA guidelines of the literature from January 1, 2015, until February 1, 2023, was performed. PubMed (Ovid), Scopus (Google), and the Cochrane Database of Systematic Reviews were searched. The literature search was limited to randomized controlled trials, systematic reviews, and meta-analyses. RESULTS: Forty-six new references were determined to be relevant. The main topics identified were (1) steroid-sparing regimens, (2) olanzapine-containing regimens, and (3) other issues such as comparisons of antiemetics of the same drug class, intravenous NK1 receptor antagonists, and potentially new antiemetics. Five updated recommendations are presented. CONCLUSION: There is no need to prescribe steroids (dexamethasone) beyond day 1 after AC HEC, whereas a 4-day regimen is recommended in non-AC HEC. Olanzapine is now recommended as a fixed part of a four-drug prophylactic antiemetic regimen in both non-AC and AC HEC. No major differences between 5-HT3 receptor antagonists or between NK1 receptor antagonists were identified. No new antiemetic agents qualified for inclusion in the updated recommendations.

Ignorable likelihood (IL) approaches are often used to handle missing data when estimating a multivariate model, such as a structural equation model. In this case, the likelihood is based on all available data, and no model is specified for the missing data mechanism. Inference proceeds via maximum likelihood or Bayesian methods, including multiple imputation without auxiliary variables. Such IL approaches are valid under a missing at random (MAR) assumption. Rabe-Hesketh and Skrondal (Ignoring non-ignorable missingness. Presidential Address at the International Meeting of the Psychometric Society, Beijing, China, 2015; Psychometrika, 2023) consider a violation of MAR where a variable A can affect missingness of another variable B also when A is not observed. They show that this case can be handled by discarding more data before proceeding with IL approaches. This data-deletion approach is similar to the sequential estimation of Mohan et al. (in: Advances in neural information processing systems, 2013) based on their ordered factorization theorem but is preferable for parametric models. Which kind of data-deletion or ordered factorization to employ depends on the nature of the MAR violation. In this article, we therefore propose two diagnostic tests, a likelihood-ratio test for a heteroscedastic regression model and a kernel conditional independence test. We also develop a test-based estimator that first uses diagnostic tests to determine which MAR violation appears to be present and then proceeds with the corresponding data-deletion estimator. Simulations show that the test-based estimator outperforms IL when the missing data problem is severe and performs similarly otherwise.

One-dimensional gas chromatography (1D-GC) stationary phases are generally classified according to their relative polarity into non-polar, semi-polar, and polar columns. In comprehensive two-dimensional gas chromatography (GC*GC), it is the polarity difference between the two tandem-assembled stationary phases that determines the selectivity of the column ensemble. This polarity difference is called orthogonality, and GC*GC column sets can be broadly categorized into four groups based on the direction of the serial coupling between the primary and secondary columns. A significant portion of the GC*GC column sets in use today are operated in forward-orthogonality mode, which means that the secondary column is more polar than the primary column. A growing number of reported GC*GC applications operate in reversed-orthogonality configurations, where the secondary column is less polar than the primary column. Very few examples exist of non-orthogonal column sets because of the fact that there is limited additional selectivity that the secondary column can offer over the separation that has already been achieved in the primary column when the two phases are either identical or close in polarity. The fourth group of GC*GC column sets, called hybrid orthogonality, involves the coupling of stationary phases with peculiar selectivity differences that manifest themselves in the bi-dimensional separation plots. In this work, we are presenting a normalized approach to GC*GC column set characterization that is based on the use of a reference mixture of standards called the Century Mix. The Century Mix contains 100 chemical probes of different functionalities that span a reasonable range of volatilities and polarities to capture the selectivity profile of any GC*GC column set. The Century Mix also contains important chemical probes that are used for 1D-GC column characterization (such as the Grab mix and the Rohrschneider/McReynolds compounds) to make some connections between the 1D-GC building block columns and the GC*GC column sets. We finally also outline some other important metrics of comparison that should be taken into consideration to assess the overall performance of a GC*GC system., THE GOAL OF ANY chromatographic method is the full separation of all analytes of interest in the shortest period of time possible, with good peakshape attributes. Therefore, column selection is [...]

Item response theory (IRT) models typically rely on a normality assumption for subject-specific latent traits, which is often unrealistic in practice. Semiparametric extensions based on Dirichlet process mixtures (DPMs) offer a more flexible representation of the unknown distribution of the latent trait. However, the use of such models in the IRT literature has been extremely limited, in good part because of the lack of comprehensive studies and accessible software tools. This article provides guidance for practitioners on semiparametric IRT models and their implementation. In particular, we rely on NIMBLE, a flexible software system for hierarchical models that enables the use of DPMs. We highlight efficient sampling strategies for model estimation and compare inferential results under parametric and semiparametric models.

The classical missing at random (MAR) assumption, as defined by Rubin (Biometrika 63:581-592, 1976), is often not required for valid inference ignoring the missingness process. Neither are other assumptions sometimes believed to be necessary that result from misunderstandings of MAR. We discuss three strategies that allow us to use standard estimators (i.e., ignore missingness) in cases where missingness is usually considered to be non-ignorable: (1) conditioning on variables, (2) discarding more data, and (3) being protective of parameters.