Recent ucla_biostatistics items

Predictors of Tobacco Use Behaviors Among Syrian Americans.

Wed, 17 Jun 2026 00:00:00 +0000

Background

This study examines the prevalence and predictors of cigarette and hookah smoking among Syrian Americans, a growing U.S. immigrant population with historically high tobacco use.

Objectives

To assess tobacco use behaviors and identify demographic and behavioral predictors of cigarette and hookah use, as well as key motivators for tobacco use, among Syrian American adults.

Design

A cross-sectional survey study of Syrian American adults in 2 U.S. states.

Methods

Data were collected from 919 Syrian American adults in Southern California and Florida between 2018 and 2019. Multinomial regression analyses were used to identify demographic and behavioral predictors of cigarette and hookah use.

Results

Among participants, 16% were current cigarette users, and 37% were current hookah users, both exceeding U.S. national averages. Social occasions and flavored tobacco were key motivators for hookah use, with most participants perceiving hookah...

Sensitive places, persistent violence: Effectiveness of “Bar Ban” laws in reducing gun violence near alcohol vendors

Wed, 6 May 2026 00:00:00 +0000

Americans have differing opinions on whether greater regulation of firearms results in improved public safety. One area that seems to enjoy broad support is to limit firearm access in specific locations. “Bar Ban” laws—which prohibit firearms where alcohol is served—represent one such approach, yet their effectiveness remains largely unexamined nationally. This paper provides the first comprehensive evaluation of the impact of Bar Ban laws on shootings near alcohol-related establishments. Using a geospatial panel dataset of over 1.6 million alcohol vendors active across the United States between January 2019 and January 2025, we analyze the relationship between restrictions on carrying a gun where alcohol is served and gun violence. Results show that shootings occur close to alcohol-serving establishments: across 263,464 shooting incidents, the median distance to the nearest alcohol vendor was 222 meters. To assess the effectiveness of Bar Ban laws, we employ a stacked difference-in-differences...

Assessing spatial disparities: a Bayesian linear regression approach

Thu, 23 Apr 2026 00:00:00 +0000

Epidemiological investigations of regionally aggregated spatial data often involve detecting spatial health disparities among neighboring regions on a map of disease mortality or incidence rates. Analyzing such data introduces spatial dependence among health outcomes and seeks to report statistically significant spatial disparities by delineating boundaries that separate neighboring regions with disparate health outcomes. However, there are statistical challenges to appropriately define what constitutes a spatial disparity and to construct robust probabilistic inferences for spatial disparities. We enrich the familiar Bayesian linear regression framework to introduce spatial autoregression and offer model-based detection of spatial disparities. We derive exploitable analytical tractability that considerably accelerates computation. Simulation experiments conducted on a county map of the entire United States demonstrate the effectiveness of our method, and we apply our method to...

Toward spatio-temporal models to support national-scale forest carbon monitoring and reporting

Wed, 22 Apr 2026 00:00:00 +0000

National forest inventory (NFI) programs provide vital information on forest parameters’ status, trend, and change. Most NFI designs and estimation methods are tailored to estimate status over large areas but are not well suited to estimate trend and change, especially over small spatial areas and/or over short time periods (e.g. annual estimates). Fine-scale space-time indexed estimates are critical to a variety of environmental, ecological, and economic monitoring efforts. In the United States, for example, NFI data are used to estimate forest carbon status, trend, and change to support national, state, and local user group needs. Increasingly, these users seek finer spatial and temporal scale estimates to evaluate existing land use policies and management practices, and inform future activities. Here we propose a spatio-temporal Bayesian small area estimation modeling framework that delivers statistically valid estimates with complete uncertainty quantification for status,...

Nonstationary Spatial Process Models with Spatially Varying Covariance Kernels

Wed, 22 Apr 2026 00:00:00 +0000

Building spatial process models that capture nonstationary behavior while delivering computationally efficient inference is challenging. Nonstationary spatially varying kernels (see, e.g., Paciorek, 2003) offer flexibility and richness, but computation is impeded by high-dimensional parameter spaces resulting from spatially varying process parameters. Matters are exacerbated if the number of locations recording measurements is massive. With limited theoretical tractability, obviating computational bottlenecks requires synergy between model construction and algorithm development. We build a class of scalable nonstationary spatial process models using spatially varying covariance kernels. We implement a Bayesian modeling framework using Hybrid Monte Carlo with nested interweaving. We conduct experiments on synthetic data sets to explore model selection and parameter identifiability, and assess inferential improvements accrued from nonstationary modeling. We illustrate strengths...

Bayesian Inference for Spatially‐Temporally Misaligned Data Using Predictive Stacking

Wed, 22 Apr 2026 00:00:00 +0000

ABSTRACT Air pollution remains a major environmental risk factor that is often associated with adverse health outcomes. However, quantifying and evaluating its effects on human health is challenging due to the complex nature of exposure data. Recent technological advances have led to the collection of various indicators of air pollution at increasingly high spatial‐temporal resolutions (e.g., daily averages of pollutant levels at spatial locations referenced by latitude‐longitude). However, health outcomes are typically aggregated over several spatial‐temporal coordinates (e.g., annual prevalence for a county) to comply with survey regulations. This article develops a Bayesian hierarchical model to analyze such spatially‐temporally misaligned exposure and health outcome data. We develop Bayesian predictive stacking for spatially and temporally misaligned data to optimally combine inference from multiple predictive spatial‐temporal models. Stacking allows us to avoid iterative...

Multivariate Spatial Process Models

Mon, 13 Apr 2026 00:00:00 +0000

Multivariate Spatial Process Models

Bayesian Nonstationary and Nonparametric Covariance Estimation for Large Spatial Data

Mon, 13 Apr 2026 00:00:00 +0000

In spatial statistics, it is often assumed that the spatial field of interest is stationary and its covariance has a simple parametric form, but these assumptions are not appropriate in many applications. Given replicate observations of a Gaussian spatial field, we propose nonstationary and nonparametric Bayesian inference on the spatial dependence. Instead of estimating the quadratic (in the number of spatial locations) entries of the covariance matrix, the idea is to infer a near-linear number of nonzero entries in a sparse Cholesky factor of the precision matrix. Our prior assumptions are motivated by recent results on the exponential decay of the entries of this Cholesky factor for Matérn-type covariances under a specific ordering scheme. Our methods are highly scalable and parallelizable. We conduct numerical comparisons and apply our methodology to climate-model output, enabling statistical emulation of an expensive physical model.

Spatial Gradients and Wombling

Mon, 13 Apr 2026 00:00:00 +0000

Spatial Gradients and Wombling

CellScope: high-performance cell atlas workflow with tree-structured representation

Thu, 2 Apr 2026 00:00:00 +0000

Single-cell sequencing enables comprehensive profiling of individual cells, revealing cellular heterogeneity and function with unprecedented resolution. However, current analysis frameworks lack the ability to simultaneously explore and visualize cellular hierarchies at multiple biological levels. To address these limitations, we present CellScope, a promising framework for constructing high-resolution cell atlases at multiple clustering levels. CellScope employs a two-stage manifold fitting process for gene selection and noise reduction, followed by agglomerative clustering, and integrates UMAP visualization with hierarchical clustering to intuitively represent cellular relationships simultaneously at multiple levels—such as cell lineage, cell type, and cell subtype levels. Compared to established pipelines such as Seurat and Scanpy, CellScope comprehensively improves clustering performance, visualization clarity, computational efficiency, and algorithm interpretability, while...

Fairness-Aware Kidney Exchange and Kidney Paired Donation

Wed, 11 Feb 2026 00:00:00 +0000

The kidney paired donation (KPD) program provides an innovative solution to overcome incompatibility challenges in kidney transplants by matching incompatible donor-patient pairs and facilitating kidney exchanges. To address unequal access to transplant opportunities, there are two widely used fairness criteria: group fairness and individual fairness. However, these criteria do not consider protected patient features, which refer to characteristics legally or ethically recognized as needing protection from discrimination, such as race and gender. Motivated by the calibration principle in machine learning, we introduce a new fairness criterion: the matching outcome should be conditionally independent of the protected feature, given the sensitization level. We integrate this fairness criterion as a constraint within the KPD optimization framework and propose a computationally efficient solution using linearization strategies and column-generation methods. Theoretically, we analyze...

Online Auction Design Using Distribution-Free Uncertainty Quantification with Applications to E-Commerce

Wed, 14 Jan 2026 00:00:00 +0000

Online auction is a cornerstone of e-commerce, and a key challenge is designing incentive-compatible mechanisms that maximize expected revenue. Existing approaches often assume known bidder value distributions and fixed sets of bidders and items, but these assumptions rarely hold in real-world settings where bidder values are unknown, and the number of future participants is uncertain. In this article, we introduce the Conformal Online Auction Design (COAD), a novel mechanism that maximizes revenue by quantifying uncertainty in bidder values without relying on known distributions. COAD incorporates both bidder and item features, using historical data to design an incentive-compatible mechanism for online auctions. Unlike traditional methods, COAD leverages distribution-free uncertainty quantification techniques and integrates machine learning methods, such as random forests, kernel methods, and deep neural networks, to predict bidder values while ensuring revenue guarantees. Moreover,...

Nonparametric estimation via partial derivatives

Wed, 14 Jan 2026 00:00:00 +0000

Traditional nonparametric estimation methods often lead to a slow convergence rate in large dimensions and require unrealistically large dataset sizes for reliable conclusions. We develop an approach based on partial derivatives, either observed or estimated, to effectively estimate the function at near-parametric convergence rates. This novel approach and computational algorithm could lead to methods useful to practitioners in many areas of science and engineering. Our theoretical results reveal behaviour universal to this class of nonparametric estimation problems. We explore a general setting involving tensor product spaces and build upon the smoothing spline analysis of variance framework. For d-dimensional models under full interaction, the optimal rates with gradient information on p covariates are identical to those for the -interaction models without gradients and, therefore, the models are immune to the curse of interaction. For additive models,...

Gridding and Parameter Expansion for Scalable Latent Gaussian Models of Spatial Multivariate Data.

Fri, 9 Jan 2026 00:00:00 +0000

Scalable spatial GPs for massive datasets can be built via sparse Directed Acyclic Graphs (DAGs) where a small number of directed edges is sufficient to flexibly characterize spatial dependence. The DAG can be used to devise fast algorithms for posterior sampling of the latent process, but these may exhibit pathological behavior in estimating covariance parameters. In this article, we introduce gridding and parameter expansion methods to improve the practical performance of MCMC algorithms in terms of effective sample size per unit time (ESS/s). Gridding is a model-based strategy that reduces the number of expensive operations necessary during MCMC on irregularly spaced data. Parameter expansion reduces dependence in posterior samples in spatial regression for high resolution data. These two strategies lead to computational gains in the big data settings on which we focus. We consider popular constructions of univariate spatial processes based on Matérn covariance functions and...

Quantifying microbial interactions based on compositional data using an iterative approach for solving generalized Lotka-Volterra equations.

Wed, 19 Nov 2025 00:00:00 +0000

Understanding microbial interactions is fundamental for exploring population dynamics, particularly in microbial communities where interactions affect stability and host health. Generalized Lotka-Volterra (gLV) models have been widely used to investigate system dynamics but depend on absolute abundance data, which are often unavailable in microbiome studies. To address this limitation, we introduce an iterative Lotka-Volterra (iLV) model, a novel framework tailored for compositional data that leverages relative abundances and iterative refinements for parameter estimation. The iLV model features two key innovations: an adaptation of the gLV framework to compositional constraints and an iterative optimization strategy combining linear approximations with nonlinear refinements to enhance parameter estimation accuracy. Using simulations and real-world datasets, we demonstrate that iLV surpasses existing methodologies, such as the compositional LV (cLV) and the generalized LV (gLV)...

Determinants of physician unwillingness to offer medical abortion using mifepristone

Wed, 19 Nov 2025 00:00:00 +0000

PURPOSE: We sought to identify factors associated with contemplating versus not contemplating offering medical abortion with mifepristone among physicians not opposed to it. METHODS: We analyzed data from a Kaiser Family Foundation survey of a nationally representative sample of 790 American obstetrician/gynecologists and primary care physicians. Our study sample consisted of 419 physicians who were not personally opposed to medical abortion and could be classified as not actively considering (precontemplation) or actively considering (contemplation) offering mifepristone. We conducted multivariate logistic regression to predict being unlikely to offer mifepristone (i.e., in the precontemplation stage of change). PRINCIPAL FINDINGS: In 2001, 1 year after U.S. Food and Drug Administration (FDA) approval, 5% of physicians surveyed were offering mifepristone. Among the 750 physicians not offering mifepristone, 57% were not opposed. Of those not opposed, 74% reported that they were...

Bayesian Geostatistics Using Predictive Stacking

Mon, 13 Oct 2025 00:00:00 +0000

We develop Bayesian predictive stacking for geostatistical models, where the primary inferential objective is to provide inference on the latent spatial random field and conduct spatial predictions at arbitrary locations. We exploit analytically tractable posterior distributions for regression coefficients of predictors and the realizations of the spatial process conditional upon process parameters. We subsequently combine such inference by stacking these models across the range of values of the hyper-parameters. We devise stacking of means and posterior densities in a manner that is computationally efficient without resorting to iterative algorithms such as Markov chain Monte Carlo (MCMC) and can exploit the benefits of parallel computations. We offer novel theoretical insights into the resulting inference within an infill asymptotic paradigm and through empirical results showing that stacked inference is comparable to full sampling-based Bayesian inference at a significantly...

Dynamic Bayesian Learning for Spatiotemp oral Mechanistic Models

Mon, 13 Oct 2025 00:00:00 +0000

Dynamic Bayesian Learning for Spatiotemp oral Mechanistic Models

HIPSTR: highest independent posterior subtree reconstruction in TreeAnnotator X

Sat, 11 Oct 2025 00:00:00 +0000

SUMMARY: In Bayesian phylogenetic and phylodynamic studies, it is common to summarize the posterior distribution of trees with a time-calibrated summary phylogeny. While the maximum clade credibility (MCC) tree is often used for this purpose, we here show that a novel summary tree method-the highest independent posterior subtree reconstruction, or (HIPSTR)-contains consistently higher supported clades over MCC. We also provide faster computational routines for estimating both summary trees in an updated version of TreeAnnotator X, an open-source software program that summarizes the information from a sample of trees and returns many helpful statistics such as individual clade credibilities contained in the summary tree. RESULTS: HIPSTR and MCC reconstructions on two Ebola virus and two SARS-CoV-2 datasets show that HIPSTR yields summary trees that consistently contain clades with higher support compared to MCC trees. The MCC trees regularly fail to include several clades with...

Inflammation and dimensions of fatigue in women with early stage breast cancer: A longitudinal examination

Sat, 11 Oct 2025 00:00:00 +0000

BACKGROUND: Fatigue is a common and long-lasting side effect of cancer. Although fatigue is a multidimensional symptom, biologic mechanisms of fatigue dimensions have not been identified. METHODS: Women recently diagnosed with early stage breast cancer (n = 192) completed assessments before and after adjuvant therapy and at 6-month, 12-month, and 18-month posttreatment follow-up visits. At each assessment, women completed the Multidimensional Fatigue Symptom Inventory and provided blood for protein markers of inflammation (tumor necrosis factor [TNF] alpha [TNF-α], soluble tumor necrosis factor receptor type II [sTNF-RII], interleukin 6 [IL-6], and C-reactive protein [CRP]). Mixed-effect linear models examined within-person and between-person associations between inflammatory markers and dimensions of fatigue. RESULTS: Analyses demonstrated a positive within-person association between general fatigue and TNF-α (b = 1.67; p = .037), sTNF-RII (b = 2.77;...

Beware of counter-intuitive levels of false discoveries in datasets with strong intra-correlations

Fri, 10 Oct 2025 00:00:00 +0000

The false discovery rate (FDR) controlling method by Benjamini and Hochberg (BH) is a popular choice in the omics fields. Here, we demonstrate that in datasets with a large degree of dependencies between features, FDR correction methods like BH can sometimes counter-intuitively report very high numbers of false positives, potentially misleading researchers. We call the attention of researchers to use suited multiple testing strategies and approaches like synthetic null data (negative control) to identify and minimize caveats related to false discoveries, as in the cases where false findings do occur, they may be numerous.

Spatial transcriptomics iterative hierarchical clustering (stIHC): A novel method for identifying spatial gene co‐expression modules

Thu, 9 Oct 2025 00:00:00 +0000

Abstract Recent advancements in spatial transcriptomics (ST) technologies allow researchers to simultaneously measure RNA expression levels for hundreds to thousands of genes while preserving spatial information within tissues, providing critical insights into spatial gene expression patterns, tissue organization, and gene functionality. However, existing methods for clustering spatially variable genes (SVGs) into co‐expression modules often fail to detect rare or unique spatial expression patterns. To address this, we present spatial transcriptomics iterative hierarchical clustering (stIHC), a novel method for clustering SVGs into co‐expression modules, representing groups of genes with shared spatial expression patterns. Through three simulations and applications to ST datasets from technologies such as 10x Visium, 10x Xenium, and Spatial Transcriptomics, stIHC outperforms clustering approaches used by popular SVG detection methods, including SPARK, SPARK‐X, MERINGUE, and SpatialDE....

Systematic benchmarking of computational methods to identify spatially variable genes

Thu, 9 Oct 2025 00:00:00 +0000

BackgroundSpatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes (SVGs). Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field.ResultsHere, we systematically evaluate 14 methods using 96 spatial datasets and 6 metrics. We compare the methods regarding gene ranking and classification based on real spatial variation, statistical calibration, and computation scalability and investigate the impact of identified SVGs on downstream applications such as spatial domain detection. Finally, we explore the applicability of the methods to spatial...

Leveraging national forest inventory data to estimate forest carbon density status and trends for small areas

Wed, 24 Sep 2025 00:00:00 +0000

Leveraging national forest inventory data to estimate forest carbon density status and trends for small areas

Bayesian Data Sketching for Varying Coefficient Regression Models.

Wed, 24 Sep 2025 00:00:00 +0000

Varying coefficient models are popular for estimating nonlinear regression functions in functional data models. Their Bayesian variants have received limited attention in large data applications, primarily due to prohibitively slow posterior computations using Markov chain Monte Carlo (MCMC) algorithms. We introduce Bayesian data sketching for varying coefficient models to obviate computational challenges presented by large sample sizes. To address the challenges of analyzing large data, we compress the functional response vector and predictor matrix by a random linear transformation to achieve dimension reduction and conduct inference on the compressed data. Our approach distinguishes itself from several existing methods for analyzing large functional data in that it requires neither the development of new models or algorithms, nor any specialized computational hardware while delivering fully model-based Bayesian inference. Well-established methods and algorithms for varying...

Randomized Controlled Trial Testing an HIV/STI Prevention Intervention Among People Leaving Incarceration Who Were Assigned Male at Birth, Have Sex with Men and A Substance Use Disorder

Wed, 30 Jul 2025 00:00:00 +0000

HIV disproportionately impacts minoritized individuals, particularly those of intersectional minoritized identities. Incarceration disproportionately impacts minoritized individuals as well, and increases HIV risk, in part due to its disruption to people’s lives, social networks, and access to care. We developed MEPS, a 6-month intervention designed to holistically support HIV prevention in men who have sex with men and transgender women leaving incarceration. We tested MEPS in a 1:1 randomized controlled trial with 208 individuals. All participants received a needs assessment and personalized wellness plan, followed by either standard of care or the MEPS intervention. MEPS integrated support from a Peer Mentor, incentives for engagement in social and health services, and a mobile app. Participants completed baseline assessments and follow-up assessments at 3, 6, and 9 months. We tested for changes in PrEP use using a group-based trajectory model, for changes in HIV and STI testing,...

Comparing PrEP use among men who have sex with men with a recent incarceration history

Thu, 10 Jul 2025 00:00:00 +0000

This study compares pre-exposure prophylaxis (PrEP) use among men who have sex with men (MSM) with a recent history of incarceration across various factors known to contribute to HIV transmission risk, including sexual identity, race/ethnicity, sexual activity, incarceration history, injection drug use, and internalized homophobia. We analyzed baseline lifetime PrEP use (yes or no) of 170 male-identifying participants enrolled in a randomized-controlled trial in Los Angeles County between 2019 and 2022. Using logistic regression, we assessed the association of PrEP with sexual identity, socio-demographics, and potential confounders. Compared to gay/same-gender loving-identified participants, straight/heterosexual-identified (aOR = 0.10, CI = 0.02-0.49) and bi-pansexual-identified (0.39, 0.16-0.95) participants had reduced odds of PrEP use. Black/African American-identified participants had lower odds (0.15, 0.03-0.78) of PrEP use than White-identified participants. Participants...

A Bayesian Time-Varying Psychophysiological Interaction Model

Thu, 3 Jul 2025 00:00:00 +0000

A Bayesian Time-Varying Psychophysiological Interaction Model

The Farm Animal Genotype–Tissue Expression (FarmGTEx) Project

Sun, 11 May 2025 00:00:00 +0000

Genetic mutation and drift, coupled with natural and human-mediated selection and migration, have produced a wide variety of genotypes and phenotypes in farmed animals. We here introduce the Farm Animal Genotype–Tissue Expression (FarmGTEx) Project, which aims to elucidate the genetic determinants of gene expression across 16 terrestrial and aquatic domestic species under diverse biological and environmental contexts. For each species, we aim to collect multiomics data, particularly genomics and transcriptomics, from 50 tissues of 1,000 healthy adults and 200 additional animals representing a specific context. This Perspective provides an overview of the priorities of FarmGTEx and advocates for coordinated strategies of data analysis and resource-sharing initiatives. FarmGTEx aims to serve as a platform for investigating context-specific regulatory effects, which will deepen our understanding of molecular mechanisms underlying complex phenotypes. The knowledge and insights provided...

scDEED: a statistical method for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters

Sun, 11 May 2025 00:00:00 +0000

Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-SNE and UMAP are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP's 2D embedding might not reliably inform the similarities among cell clusters. Motivated by this challenge, we developed a statistical method, scDEED, for detecting dubious cell embeddings output by any 2D-embedding method. By calculating a reliability score for every cell embedding, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. Applied to multiple scRNA-seq datasets, scDEED demonstrates its effectiveness for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.

Comment on “Data Fission: Splitting a Single Data Point” Data Fission for Unsupervised Learning: A Discussion on Post-Clustering Inference and the Challenges of Debiasing

Sun, 11 May 2025 00:00:00 +0000

Comment on “Data Fission: Splitting a Single Data Point” Data Fission for Unsupervised Learning: A Discussion on Post-Clustering Inference and the Challenges of Debiasing

Individualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials.

Thu, 17 Apr 2025 00:00:00 +0000

BACKGROUND: The cardiovascular benefits of intensive systolic blood pressure control vary across clinical populations tested in large randomised clinical trials. We aimed to evaluate the application of machine learning to clinical trials of patients without and with type 2 diabetes to define the personalised cardiovascular benefit of intensive control of systolic blood pressure. METHODS: In SPRINT, a trial of intensive (systolic blood pressure <120 mm Hg) versus standard (systolic blood pressure <140 mm Hg) systolic blood pressure control in patients without type 2 diabetes, we defined a phenotypic representation of the study population using 59 baseline variables. We extracted personalised treatment effect estimates for the primary outcome, time-to-first major adverse cardiovascular event (MACE; cardiovascular death, myocardial infarction or acute coronary syndrome, stroke, and acute decompensated heart failure), through iterative Cox regression analyses providing average...

Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research

Tue, 15 Apr 2025 00:00:00 +0000

The Health-Analytics Data to Evidence Suite (HADES) is an open-source software collection developed by Observational Health Data Sciences and Informatics (OHDSI). It executes directly against healthcare data such as electronic health records and administrative claims, that have been converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Using advanced analytics, HADES performs characterization, population-level causal effect estimation, and patient-level prediction, potentially across a federated data network, allowing patient-level data to remain locally while only aggregated statistics are shared. Designed to run across a wide array of technical environments, including different operating systems and database platforms, HADES uses continuous integration with a large set of unit tests to maintain reliability. HADES implements OHDSI best practices, and is used in almost all published OHDSI studies, including some that have directly informed regulatory...

Comparative safety and effectiveness of angiotensin converting enzyme inhibitors and thiazides and thiazide‐like diuretics under strict monotherapy

Tue, 15 Apr 2025 00:00:00 +0000

Previous work comparing safety and effectiveness outcomes for new initiators of angiotensin converting-enzyme inhibitors (ACEi) and thiazides demonstrated more favorable outcomes for thiazides, although cohort definitions allowed for addition of a second antihypertensive medication after a week of monotherapy. Here, we modify the monotherapy definition, imposing exit from cohorts upon addition of another antihypertensive medication. We determine hazard ratios (HR) for 55 safety and effectiveness outcomes over six databases and compare results to earlier findings. We find, for all primary outcomes, statistically significant differences in effectiveness between ACEi and thiazides were not replicated (HRs: 1.11, 1.06, 1.12 for acute myocardial infarction, hospitalization with heart failure and stroke, respectively). While statistical significance is similarly lost for several safety outcomes, the safety profile of thiazides remains more favorable. Our results indicate a less striking...

Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms

Sat, 12 Apr 2025 00:00:00 +0000

RNA splicing is highly prevalent in the brain and has strong links to neuropsychiatric disorders; yet, the role of cell type-specific splicing and transcript-isoform diversity during human brain development has not been systematically investigated. In this work, we leveraged single-molecule long-read sequencing to deeply profile the full-length transcriptome of the germinal zone and cortical plate regions of the developing human neocortex at tissue and single-cell resolution. We identified 214,516 distinct isoforms, of which 72.6% were novel (not previously annotated in Gencode version 33), and uncovered a substantial contribution of transcript-isoform diversity-regulated by RNA binding proteins-in defining cellular identity in the developing neocortex. We leveraged this comprehensive isoform-centric gene annotation to reprioritize thousands of rare de novo risk variants and elucidate genetic risk mechanisms for neuropsychiatric disorders.

Methods for the Analysis of 26 Million VOC Area Measurements during the Deepwater Horizon Oil Spill Clean-up

Fri, 11 Apr 2025 00:00:00 +0000

The NIEHS GuLF STUDY is an epidemiologic study of the health of workers who participated in the 2010 Deepwater Horizon oil spill response and clean-up effort. Even with a large database of approximately 28 000 personal samples that were analyzed for total hydrocarbons (THCs) and other oil-related chemicals, resulting in nearly 160 000 full-shift personal measurements, there were still exposure scenarios where the number of measurements was too limited to rigorously assess exposures. Also available were over 26 million volatile organic compounds (VOCs) area air measurements of approximately 1-min duration, collected from direct-reading instruments on 38 large vessels generally located near the leaking well. This paper presents a strategy for converting the VOC database into hourly average air concentrations by vessel as the first step of a larger process designed to use these data to supplement full-shift THC personal exposure measurements. We applied a Bayesian method to account...

Exposure Assessment Techniques Applied to the Highly Censored Deepwater Horizon Gulf Oil Spill Personal Measurements

Fri, 11 Apr 2025 00:00:00 +0000

The GuLF Long-term Follow-up Study (GuLF STUDY) is investigating potential adverse health effects of workers involved in the Deepwater Horizon (DWH) oil spill response and cleanup (OSRC). Over 93% of the 160 000 personal air measurements taken on OSRC workers were below the limit of detection (LOD), as reported by the analytic labs. At this high level of censoring, our ability to develop exposure estimates was limited. The primary objective here was to reduce the number of measurements below the labs' reported LODs to reflect the analytic methods' true LODs, thereby facilitating the use of a relatively unbiased and precise Bayesian method to develop exposure estimates for study exposure groups (EGs). The estimates informed a job-exposure matrix to characterize exposure of study participants. A second objective was to develop descriptive statistics for relevant EGs that did not meet the Bayesian criteria of sample size ≥5 and censoring ≤80% to achieve the aforementioned level of...

Fixed-Domain Asymptotics Under Vecchia's Approximation of Spatial Process Likelihoods.

Fri, 11 Apr 2025 00:00:00 +0000

Statistical modeling for massive spatial data sets has generated a substantial literature on scalable spatial processes based upon Vecchia's approximation. Vecchia's approximation for Gaussian process models enables fast evaluation of the likelihood by restricting dependencies at a location to its neighbors. We establish inferential properties of microergodic spatial covariance parameters within the paradigm of fixed-domain asymptotics when they are estimated using Vecchia's approximation. The conditions required to formally establish these properties are explored, theoretically and empirically, and the effectiveness of Vecchia's approximation is further corroborated from the standpoint of fixed-domain asymptotics.

Linear Relationships Between Total Hydrocarbons and Benzene, Toluene, Ethylbenzene, Xylene, and n-Hexane during the Deepwater Horizon Response and Clean-up

Fri, 11 Apr 2025 00:00:00 +0000

OBJECTIVES: Our objectives were to (i) determine correlations between measurements of THC and of BTEX-H, (ii) apply these linear relationships to predict BTEX-H from measured THC, (iii) use these correlations as informative priors in Bayesian analyses to estimate exposures. METHODS: We used a Bayesian left-censored bivariate framework for all 3 objectives. First, we modeled the relationships (i.e. correlations) between THC and each BTEX-H chemical for various overarching groups of measurements using linear regression to determine if correlations derived from linear relationships differed by various exposure determinants. We then used the same linear regression relationships to predict (or impute) BTEX-H measurements from THC when only THC measurements were available. Finally, we used the same linear relationships as priors for the final exposure models that used real and predicted data to develop exposure estimate statistics for each individual exposure group. RESULTS: Correlations...

Estimates of Inhalation Exposures to Oil-Related Components on the Supporting Vessels During the Deepwater Horizon Oil Spill

Fri, 11 Apr 2025 00:00:00 +0000

The Deepwater Horizon oil spill response and clean-up (OSRC) involved over 9000 large and small vessels deployed in waters of the Gulf of Mexico across four states (Alabama, Florida, Louisiana, and Mississippi). For the GuLF STUDY, we developed exposure estimates of oil-related components for many work groups to capture a wide range of OSRC operations on these vessels, such as supporting the four rig vessels charged with stopping the spill at the wellhead; skimming oil; in situ burning of oil; absorbing and containing oil by boom; and environmental monitoring. Work groups were developed by: (i) vessel activity; (ii) location (area of the Gulf or state); and (iii) time period. Using Bayesian methods, we computed exposure estimates for these groups for: total hydrocarbons measured as total petroleum hydrocarbons (THC), benzene, toluene, ethylbenzene, xylene, and n-hexane (BTEX-H). Estimates of the arithmetic means for THC ranged from 0.10 ppm [95% credible interval (CI) 0.04, 0.38...

Exposure Group Development in Support of the NIEHS GuLF Study

Fri, 11 Apr 2025 00:00:00 +0000

In the GuLF Study, a study investigating possible adverse health effects associated with work on the oil spill response and clean-up (OSRC) following the Deepwater Horizon disaster in the Gulf of Mexico, we used a job-exposure matrix (JEM) approach to estimate exposures. The JEM linked interview responses of study participants to measurement data through exposure groups (EGs). Here we describe a systematic process used to develop transparent and precise EGs that allowed characterization of exposure levels among the large number of OSRC activities performed across the Gulf of Mexico over time and space. EGs were identified by exposure determinants available to us in our measurement database, from a substantial body of other spill-related information, and from responses provided by study participants in a detailed interview. These determinants included: job/activity/task, vessel and type of vessel, weathering of the released oil, area of the Gulf of Mexico, Gulf coast state, and...

spNNGP R Package for Nearest Neighbor Gaussian Process Models