Maternal exposure to SSRIs or SNRIs and the risk of congenital abnormalities in offspring: A systematic review and meta-analysis.

Background: The association of maternal exposure to selective serotonin reuptake inhibitors (SSRIs) or serotonin and norepinephrine reuptake inhibitors (SNRIs) with the risk of system-specific congenital malformations in offspring remains unclear. We conducted a meta-analysis to examine this association and the risk difference between these two types of inhibitors.

Methods: A literature search was performed from January 2000 to May 2023 using PubMed and Web of Science databases.

Single-cell transcriptomics shows dose-dependent disruption of hepatic zonation by TCDD in mice.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) dose-dependently induces the development of hepatic fat accumulation and inflammation with fibrosis in mice initially in the portal region. Conversely, differential gene and protein expression is first detected in the central region. To further investigate cell-specific and spatially resolved dose-dependent changes in gene expression elicited by TCDD, single-nuclei RNA sequencing and spatial transcriptomics were used for livers of male mice gavaged with TCDD every 4 days for 28 days.

Fitness cost conferred by the novel erm(51) and rpoB mutation on environmental multidrug resistant-Rhodococcus equi.

Rhodococcus equi is a common cause of severe pneumonia in foals. Emergence of macrolide-resistant R. equi isolated from foals and their environment has been reported in the United States.

Benchmarking of a Bayesian single cell RNAseq differential gene expression test for dose-response study designs.

The application of single-cell RNA sequencing (scRNAseq) for the evaluation of chemicals, drugs, and food contaminants presents the opportunity to consider cellular heterogeneity in pharmacological and toxicological responses. Current differential gene expression analysis (DGEA) methods focus primarily on two group comparisons, not multi-group dose-response study designs used in safety assessments. To benchmark DGEA methods for dose-response scRNAseq experiments, we proposed a multiplicity corrected Bayesian testing approach and compare it against 8 other methods including two frequentist fit-for-purpose tests using simulated and experimental data.

Identification of miR-203a, mir-10a, and miR-194 as predictors for risk of lymphovascular invasion in head and neck cancers.

Lymphovascular invasion (LVI) is an important prognostic indicator of lymph node metastasis and disease aggressiveness but clear molecular mechanisms mediating this in head and neck cancers (HNSC) remain undefined. To identify important microRNAs (miRNAs) in HNSC that associate with and are also predictive of increased risk of LVI, we used a combination of clustering algorithms, multiple regression analyses and machine learning approaches and analyzed miRNA expression profiles in the TCGA HNSC database. As the first step, we identified miRNAs with increased association with LVI as a binary variable.

A comparative study of two-sample tests for interval-censored data.

Interval-censored data are ubiquitous in clinical studies where actual time-to-event is difficult to measure. A number of nonparametric tests have been proposed to conduct a two-sample test using interval-censored data, and these tests can be used for assessing and comparing treatment effects over the control group. Alternatively, as commonly perceived, parametric tests can also be used assuming data are generated from a parametric family of distributions.

CXCL11-CXCR3 Axis Mediates Tumor Lymphatic Cross Talk and Inflammation-Induced Tumor, Promoting Pathways in Head and Neck Cancers.

Tumor metastasis to the draining lymph nodes is critical in patient prognosis and is tightly regulated by molecular interactions mediated by lymphatic endothelial cells (LECs). The underlying mechanisms remain undefined in the head and neck squamous cell carcinomas (HNSCCs). Using HNSCC cells and LECs we determined the mechanisms mediating tumor-lymphatic cross talk.

A test of homogeneity of distributions when observations are subject to measurement errors.

When the observed data are contaminated with errors, the standard two-sample testing approaches that ignore measurement errors may produce misleading results, including a higher type-I error rate than the nominal level. To tackle this inconsistency, a nonparametric test is proposed for testing equality of two distributions when the observed contaminated data follow the classical additive measurement error model. The proposed test takes into account the presence of errors in the observed data, and the test statistic is defined in terms of the (deconvoluted) characteristic functions of the latent variables.

Searching for Beyond the Standard Model Physics with COHERENT Energy and Timing Data.

We search for beyond the standard model physics by combining COHERENT Collaboration energy and timing data. Focusing on light, ≲GeV mediators, we find the data favor a ∼10-1000  MeV mediator, as compared to the standard model best fit at the ≲2σ level. The best-fit coupling range is g∼10^{-5}-10^{-3}.

Analysis of linear transformation models with covariate measurement error and interval censoring.

Among several semiparametric models, the Cox proportional hazard model is widely used to assess the association between covariates and the time-to-event when the observed time-to-event is interval-censored. Often, covariates are measured with error. To handle this covariate uncertainty in the Cox proportional hazard model with the interval-censored data, flexible approaches have been proposed.

A consistent estimator for logistic mixed effect models.

We propose a consistent and locally efficient estimator to estimate the model parameters for a logistic mixed effect model with random slopes. Our approach relaxes two typical assumptions: the random effects being normally distributed, and the covariates and random effects being independent of each other. Adhering to these assumptions is particularly difficult in health studies where in many cases we have limited resources to design experiments and gather data in long-term studies, while new findings from other fields might emerge, suggesting the violation of such assumptions.

Matched case-control data with a misclassified exposure: what can be done with instrumental variables?

Matched case-control studies are used for finding the association between a disease and an exposure after controlling the effect of important confounding variables. It is a known fact that the disease-exposure association parameter estimators are biased when the exposure is misclassified, and a matched case-control study is of no exception. Any bias correction method relies on validation data that contain the true exposure and the misclassified exposure value, and in turn the validation data help to estimate the misclassification probabilities.

Two Wrongs Make a Right: Addressing Underreporting in Binary Data from Multiple Sources.

Media-based event data-i.e., data comprised from reporting by media outlets-are widely used in political science research.

Frequentist Standard Errors of Bayes Estimators.

Frequentist standard errors are a measure of uncertainty of an estimator, and the basis for statistical inferences. Frequestist standard errors can also be derived for Bayes estimators. However, except in special cases, the computation of the standard error of Bayesian estimators requires bootstrapping, which in combination with Markov chain Monte Carlo (MCMC) can be highly time consuming.

Bayesian variable selection in the accelerated failure time model with an application to the surveillance, epidemiology, and end results breast cancer data.

Accelerated failure time model is a popular model to analyze censored time-to-event data. Analysis of this model without assuming any parametric distribution for the model error is challenging, and the model complexity is enhanced in the presence of large number of covariates. We developed a nonparametric Bayesian method for regularized estimation of the regression parameters in a flexible accelerated failure time model.

Functional Mixed Effects Model for Small Area Estimation.

Functional data analysis has become an important area of research due to its ability of handling high dimensional and complex data structures. However, the development is limited in the context of linear mixed effect models, and in particular, for small area estimation. The linear mixed effect models are the backbone of small area estimation.

Semiparametric Bayesian analysis of censored linear regression with errors-in-covariates.

The accelerated failure time (AFT) model is a well-known alternative to the Cox proportional hazard model for analyzing time-to-event data. In this paper we consider fitting an AFT model to right censored data when a predictor variable is subject to measurement errors. First, without measurement errors, estimation of the model parameters in the AFT model is a challenging task due to the presence of censoring, especially when no specific assumption is made regarding the distribution of the logarithm of the time-to-event.

Analysis of Multivariate Disease Classification Data in the Presence of Partially Missing Disease Traits.

In modern cancer epidemiology, diseases are classified based on pathologic and molecular traits, and different combinations of these traits give rise to many disease subtypes. The effect of predictor variables can be measured by fitting a polytomous logistic model to such data. The differences (heterogeneity) among the relative risk parameters associated with subtypes are of great interest to better understand disease etiology.

Semiparametric approach for non-monotone missing covariates in a parametric regression model.

Missing covariate data often arise in biomedical studies, and analysis of such data that ignores subjects with incomplete information may lead to inefficient and possibly biased estimates. A great deal of attention has been paid to handling a single missing covariate or a monotone pattern of missing data when the missingness mechanism is missing at random. In this article, we propose a semiparametric method for handling non-monotone patterns of missing data.

Semiparametric analysis of linear transformation models with covariate measurement errors.

We take a semiparametric approach in fitting a linear transformation model to a right censored data when predictive variables are subject to measurement errors. We construct consistent estimating equations when repeated measurements of a surrogate of the unobserved true predictor are available. The proposed approach applies under minimal assumptions on the distributions of the true covariate or the measurement errors.

Bias reduction in conditional logistic regression.

We employ a general bias preventive approach developed by Firth (Biometrika 1993; 80:27-38) to reduce the bias of an estimator of the log-odds ratio parameter in a matched case-control study by solving a modified score equation. We also propose a method to calculate the standard error of the resultant estimator. A closed-form expression for the estimator of the log-odds ratio parameter is derived in the case of a dichotomous exposure variable.

Analysis of cohort studies with multivariate and partially observed disease classification data.

Complex diseases like cancers can often be classified into subtypes using various pathological and molecular traits of the disease. In this article, we develop methods for analysis of disease incidence in cohort studies incorporating data on multiple disease traits using a two-stage semiparametric Cox proportional hazards regression model that allows one to examine the heterogeneity in the effect of the covariates by the levels of the different disease traits. For inference in the presence of missing disease traits, we propose a generalization of an estimating equation approach for handling missing cause of failure in competing-risk data.

Missing exposure data in stereotype regression model: application to matched case-control study with disease subclassification.

With advances in modern medicine and clinical diagnosis, case-control data with characterization of finer subtypes of cases are often available. In matched case-control studies, missingness in exposure values often leads to deletion of entire stratum, and thus entails a significant loss in information. When subtypes of cases are treated as categorical outcomes, the data are further stratified and deletion of observations becomes even more expensive in terms of precision of the category-specific odds-ratio parameters, especially using the multinomial logit model.

Semiparametric bayesian analysis of nutritional epidemiology data in the presence of measurement error.

We propose a semiparametric Bayesian method for handling measurement error in nutritional epidemiological data. Our goal is to estimate nonparametrically the form of association between a disease and exposure variable while the true values of the exposure are never observed. Motivated by nutritional epidemiological data, we consider the setting where a surrogate covariate is recorded in the primary data, and a calibration data set contains information on the surrogate variable and repeated measurements of an unbiased instrumental variable of the true exposure.

Inference of the haplotype effect in a matched case-control study using unphased genotype data.

Typically locus specific genotype data do not contain information regarding the gametic phase of haplotypes, especially when an individual is heterozygous at more than one locus among a large number of linked polymorphic loci. Thus, studying disease-haplotype association using unphased genotype data is essentially a problem of handling a missing covariate in a case-control design. There are several methods for estimating a disease-haplotype association parameter in a matched case-control study.