Biometrics

Disease boundary analysis identifies abrupt changes in health outcomes across geographic boundaries, guiding targeted public health interventions and outbreak surveillance. Current implementations often adopt a Bayesian "wombling" approach and largely rely on Markov Chain Monte Carlo (MCMC) posterior sampling, presenting scalability issues for large-scale disease surveillance. We leverage amortized Bayesian inference (ABI) to accelerate the detection of spatial health disparities between neighboring US counties by embedding neural posterior estimation within a Bayesian areal wombling framework. Exploiting the computational efficiency of ABI, we further introduce the Residual Disparity Elimination Target, a metric for the required reduction in mortality or prevalence for a region to eliminate a significant disparity with its neighbor. We analyze tracheal, bronchus, and lung cancer mortality rates across mainland US counties and achieve results concordant with MCMC analysis while scaling areal wombling to hundreds of outcomes and translating disparity detection into interpretable policy objectives.

In competing risks settings, covariate effects and group comparisons are usually assessed one event at a time - through log-rank or Cox tests on the cause-specific hazards, or Gray's test or Fine-Gray regression on a cumulative incidence function (CIF). This can obscure a clinically important quantity: the ratio between the event of interest and the competing event, since groups may differ little on the individual events yet differ sharply in their ratio. The generalized competing event (GCE) framework makes this ratio the object of inference; on the cause-specific scale the hazard ratio omega+(t) = lambda_1(t)/lambda_2(t) is estimated efficiently from a single stacked (Lunn-McNeil) model. We extend the framework to two scales that describe realized incidence. The subdistribution hazard ratio omega-tilde+(t) = lambda-tilde_1(t)/lambda-tilde_2(t) is estimated by a stacked, risk-set-weighted extension of the Lunn-McNeil construction; the cumulative-incidence ratio rho(t) = F_1(t)/F_2(t) - the odds that a subject's realized event by time t is the event of interest - by jackknife pseudo-observation regression of the Aalen-Johansen estimator. We relate the three contrasts: rho equals omega+ exactly under proportional cause-specific hazards, and equals omega-tilde+ only in the small-time limit under proportional subdistribution hazards, drifting toward 1 thereafter. The orthogonality that makes omega+ efficient is lost on both cumulative-incidence scales - omega tilde+ through overlapping weighted risk sets and shared censoring weights, rho through the shared all-cause survivor - so each carries a covariance term that must be handled and that bounds efficiency relative to the hazard-scale test. We derive the corresponding variances, study operating characteristics by simulation, illustrate on hypothetical prostate and head-and-neck cohorts, and provide an implementation in the gcemod R package.

Probabilistic latent variable models are a powerful tool for uncovering structure in high-dimensional datasets, particularly in biomedical applications. The increasing availability of large-scale epidemiological studies, such as the UK Biobank, poses important modelling challenges, including mixed data types, high dimensionality, and structured missingness. Existing approaches address some of these issues, but few provide a unified and scalable framework for handling them simultaneously. Here, we propose a scalable Bayesian factor analysis framework designed to address these challenges. Our method combines a semi-parametric Gaussian copula model with a continuous spike-and-slab prior to induce sparse and interpretable factor loadings. The number of latent dimensions is learned nonparametrically from the data using an Indian buffet process prior. For model fitting, we develop an expectation-maximisation algorithm that naturally accommodates missing data. We validate the proposed method through comprehensive simulation studies. In addition, we showcase the proposed model using the Novartis-Oxford Multiple Sclerosis dataset in two ways. First, we identify latent dimensions shared across MS clinical and neuroimaging variables, characterising disease structure while demonstrating the models ability to handle multiple data types and structured missingness. Second, we use the model for dimensionality reduction of structural MRI data, extracting features for downstream analysis that go beyond traditional whole-brain summary statistics. In those applications, our method identifies sparse latent structures and provides insights beyond those obtained from traditional approaches.

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have been linked to heterogeneous, potentially pleiotropic effects across organ systems, motivating outcome-wide comparative risk profiling in real-world data. A central challenge in such analyses is \emph{residual bias} that remains after adjustment for observed confounders, which can distort effect estimates and mis-calibrate uncertainty. We present distributional diagnosis and calibration (DC), which uses panels of negative control outcomes (NCOs) to diagnose residual bias and calibrate uncertainty. DC evaluates null behavior via $p$-value uniformity and empirical coverage across NCOs, and uses the empirical distribution of NCO effect estimates to calibrate confidence intervals for prespecified primary outcomes. DC is modular: it can wrap around commonly used causal inference methods and operates directly on summary statistics, supporting collaborative research under data-sharing constraints. Using electronic health records from a large U.S. clinical research network (152.7 million patients), we compared GLP-1RAs with sodium--glucose cotransporter~2 inhibitors across 15 prespecified outcomes spanning cardiovascular, mental health, and genitourinary domains using four causal estimators. Across outcomes and methods, DC diagnostics revealed substantial and method-dependent residual systematic error. DC calibration attenuated systematic error signals observed in negative controls and yielded more stable, better-calibrated estimates for clinical outcomes, supporting DC as a practical strategy to strengthen the credibility of real-world comparative effectiveness research.

Elective surgery late cancellations and ``did not attend'' (LCDNA) events waste theatre capacity, lengthen waiting lists, and impose avoidable costs on NHS Trusts. We present a decision-support approach that ranks upcoming elective procedures by expected cancellation cost and supports capacity-constrained outreach by selecting the highest-risk Top-K cases for intervention. Using cost-sensitive learning and a clinically grounded cost model, the policy reduces expected cost from approximately 103 GBP per case under business-as-usual to 77.08 GBP per case in a hospital-holdout (cross-site) evaluation designed to mimic deployment to a new hospital. In a complementary time-forward evaluation, representing prospective use within the same service environment, expected cost falls further to 70.97 GBP per case. The 6.11 GBP per-case difference between the two regimes highlights the added uncertainty introduced by cross-site operational shift and supports a conservative roll-out with local calibration and monitoring. Explainability analyses suggest that booking-to-procedure lead time, specialty or service line, calendar effects, and prior cancellation history are the strongest drivers of prediction, helping to inform tiered intervention workflows that prioritise near-term bookings and use model--pathway mismatches as an audit signal. Overall, the framework turns predictive performance into practical, capacity-aware policy guidance for reducing avoidable cancellations while supporting safe and equitable implementation.

Instrumental variable (IV) methods are widely used in health and social sciences to estimate causal treatment effects among compliers. In certain research settings, the instrument-treatment association (first stage) and the instrument-outcome association (reduced form) are each estimated from a different dataset. Two-Sample Instrumental Variables (TSIV), proposed by Angrist and Krueger (1992), addresses this by combining first-stage and reduced-form estimates from separate data sources into a single causal effect estimate. However, TSIV identification requires that instrument compliance behavior be consistent across the two samples, a condition that is rarely verified in practice. We show mathematically and empirically that when compliance differs between samples, the raw TSIV estimator does not converge to the true Local Average Treatment Effect (LATE) and instead attenuates toward a predictably biased limit proportional to the ratio of first-stage compliance rates between the two samples. To address this, we formalize a framework for estimating LATE with TSIV under two key assumptions: (1) Covariate Overlap, requiring that the two samples share sufficient common support in their covariate distributions, and (2) Compliance Transportability, requiring that compliance behavior is identical across populations after conditioning on observed covariates. We consider a setting in which a health policy instrument and outcomes are recorded in administrative claims while treatment and covariates are collected in a survey. We use a C-statistic derived from pooled covariates to detect population mismatch and an Inverse Probability Weighting (IPW) correction that reweights the first-stage sample to approximate the administrative covariate distribution. In Monte Carlo simulations across eight scenarios calibrated to a survey-Medicaid setting, IPW-TSIV reduces bias in estimating the LATE, achieving 88% reduction in the primary scenario, 82% under severe selection, and 79% when state-level expansion policy drives compliance heterogeneity. We further validate this framework using the Oregon Health Insurance Experiment, where partitioning the public-use lottery data (N = 24,646) into two non-overlapping samples with substantively meaningful compliance heterogeneity yields a verifiable benchmark against the true causal effect. IPW-TSIV reduces mean absolute bias by 71.6% relative to the oracle S2-specific LATE across 10 independent replications (C-statistic = 0.78), outperforms naive TSIV in all 10 splits, and reduces mean bias relative to the full-data LATE from +0.016 to +0.008. This framework provides applied researchers with actionable diagnostic thresholds to detect sample mismatch, validate transportability assumptions, and determine when structural TSIV estimation is reliable.

Constructing microbial association networks is a common strategy for exploring relationships among taxa in microbiome studies. Although marginal correlation methods are easy to implement and allow formal inference, they can produce spurious edges driven by indirect associations through other taxa. Conditional graphical-modeling methods aim to recover direct associations, but many rely on Gaussian or linear assumptions and often provide limited uncertainty quantification. We propose a conditional, nonparametric approach based on the scaled expected conditional covariance (SEcov). SEcov measures population-level conditional association by residualizing each taxon with respect to the remaining taxa and scaling the resulting expected conditional covariance. The resulting estimator can incorporate flexible machine-learning methods for conditional-mean estimation and admits asymptotic normal inference, enabling p-values and confidence intervals for taxon-pair associations. We demonstrate through simulation studies that our proposed approach improves network recovery relative to other methods, and we illustrate the new method via construction of a co-occurrence network for the vaginal microbiome during pregnancy. IMPORTANCEHigh-throughput sequencing has made it possible to characterize microbial communities at large scale, and network analysis is widely used to summarize relationships among taxa. However, networks based on marginal correlations may include indirect associations, whereas many conditional graphical models rely on assumptions that may be difficult to justify for sparse, zero-inflated, compositional microbiome data. SEcov offers a practical alternative by estimating conditional associations nonparametrically and attaching inferential uncertainty to individual edges. This allows investigators to construct microbiome networks using statistically interpretable evidence for taxon-pair associations, rather than relying solely on arbitrary correlation cutoffs or regularization tuning parameters.

Abstract: Compartmental epidemic models conventionally treat the probability of moving between dis ease states as fixed over time, an assumption that sits uneasily with the reality of a pandemic in which lockdowns, mask mandates, vaccination roll-out, and the arrival of new variants continually reshape transmission. This paper develops a time-inhomogeneous Markov chain framework for the Susceptible-Exposed-Infectious-Removed (SEIR) process, in which each transition probability pab(t) is allowed to vary with calendar time while respecting the struc tural zeros implied by the SEIR compartmental flow. We derive the constrained maximum likelihood estimator of pab(t) under these structural constraints, establish its finite sample efficiency, asymptotic normality, and Wilson score confidence intervals, and construct a like lihood ratio test of the null hypothesis that a compartments exit probability is constant over time. We further propose a stochastic machine learning hybrid extension in which the raw, kernel smoothed transition probabilities are regressed on policy and mobility covariates using both a logistic generalized linear model and a random forest, allowing the framework to attribute time-inhomogeneity to observable interventions. The methodology is applied to a compiled daily, district level COVID-19 surveillance panel for Sierra Leone spanning March 2020 to December 2023 (16 districts, 1,401 days). The likelihood ratio test rejects time-homogeneity of the exposed to infectious transition in 15 of 16 districts and of the infectious-to-removed transition in 8 of 16 districts ( = 0.05), and the covariate augmented logistic model achieves an out of sample Brier score roughly 76 times smaller than a time homogeneous pooled baseline, with healthcare capacity and the time trend emerging as the most influential predictors in the random-forest component. These results provide statisti cal evidence that time-inhomogeneous, covariate informed Markov models offer a materially better description of district-level COVID-19 transmission in Sierra Leone than classical time homogeneous compartmental models, with implications for sub-national outbreak monitoring in resource constrained settings

As machine learning and artificial intelligence systems are increasingly used in healthcare, rigorous evaluation of their classification performance has become critical. The F1 and F{beta} scores are widely adopted metrics for assessing performance in imbalanced biomedical data. Recently, we introduced psF1, a unified statistical framework for inference and study design for single and comparative F1 and F{beta} scores under the assumption of independent classifiers. In practice, however, benchmarking two classifiers on the same dataset creates a correlated paired setting. Ignoring this intrinsic dependency leads to overestimation of the standard error and a substantial loss of statistical power. To address this, we develop psF1pair, an advanced framework for statistical inference and power analysis that explicitly accounts for correlations between classifier pairs. Extensive simulation studies demonstrate the performance of psF1pair, and its utility is further illustrated through application to a real-world imaging classification system. As expected, higher correlation between classifiers yields narrower confidence intervals and enhanced statistical power. A freely available R package is provided to facilitate implementation, supporting accurate evaluation and study design for predictive and classification models in biomedical research.

BackgroundMediation analysis of high-dimensional features, particularly molecular-level omics features, provides important opportunities to uncover biological mechanisms underlying human health and disease. However, two central statistical challenges remain: testing the composite-null hypothesis and maintaining power when the exposure-mediator and mediator-outcome associations differ substantially in statistical significance. Existing methods typically rely on accurate estimation of the proportions of the three null types or on the maximum of the two association p-values, and may not always control the FDR well and may have limited power under imbalanced significance. MethodsWe propose SMS, a new statistical framework based on symmetric mediation statistics. By exploiting symmetry, SMS calibrates the composite null distribution as a whole for FDR control. It also allows flexible combinations of the two association p-values, including the maximum, and then enables construction of an omnibus test. Moreover, it permits direct use of effect-size estimates, bypassing the need to compute p-values. ResultsSMS controlled the FDR across a wide range of simulation scenarios while achieving a substantial sensitivity gain, often around 20 percentage points, over existing methods including HDMT, DACT, and DEI-B. Applications to a metabolomics dataset and a DNA methylation dataset further corroborated these findings. Notably, SMS discovered five plausible mediators in the metabolomics dataset that were missed by all existing methods considered.

Spatial transcriptomics technologies enable measurement of gene expression while preserving spatial tissue organization, but they remain highly sensitive to technical variability such as library size differences, slide-level effects, and spatial artifacts. Most existing normalization approaches treat normalization as a preprocessing step and perform downstream analyses on normalized values as fixed inputs, ignoring the uncertainty introduced during normalization. We introduce GPSNorm (Gaussian Process Spatial Normalization), a Bayesian spatial normalization framework that jointly models technical variation, spatial structure, and biological signal within a unified hierarchical model. GPSNorm represents gene expression counts using a negative binomial latent Gaussian model whose spatial component is a Gaussian Markov random field approximating a Gaussian process and performs efficient approximate Bayesian inference using the Integrated Nested Laplace Approximation (INLA), producing posterior estimates that propagate normalization uncertainty into downstream differential expression analysis. In simulations anchored to empirical spatial transcriptomics data, GPSNorm accurately recovers spatial technical structure and improves log-fold change estimation compared with existing normalization methods. Applications to three spatial transcriptomics datasets--including human dorsolateral prefrontal cortex Visium data, a GeoMx COVID-19 lung damage study, and the Spatial Organ Atlas kidney dataset--demonstrate improved preservation of biologically expected spatial patterns and marker gene contrasts. These results show that jointly modeling normalization and downstream inference can improve the robustness and interpretability of spatial transcriptomics analyses.An open-source R implementation of GPSNorm is available at https://github.com/Tiny-Quant/GPSNorm.

Accurate normalization is essential for differential expression analysis of RNA-sequencing data. Popular normalization methods such as the median-of-ratios and trimmed mean of M-values do not leverage information from the experimental design. This may be inefficient in experiments with large-scale systematic expression changes or complex designs. Here, we introduce design-informed size factor estimation (disize), a normalization method that uses information from the experimental design to improve accuracy. disize uses a modified generalized linear mixed model to robustly distinguish between biological signal and sample-specific size factors. We also propose a mechanistically justified data-generating process for RNA-sequencing counts that is derived from previous models of transcription and sequencing. Through simulations based on this data-generating process and validating on true RNA-seq data, we show that disize recovers size factors more accurately than existing methods, particularly in challenging scenarios with low gene expression and a high proportion of differentially expressed genes; this in turn improves downstream analysis. disize provides a robust and accurate approach to normalization, highlighting the significant benefits of integrating experimental design information directly into normalization for transcriptomic datasets. Author summaryIn transcriptomic analysis, normalization adjusts for technical biases arising from library preparation and sequencing. Methods implemented in widely used packages like DESeq2 and edgeR ignore information in the experimental design during normalization. Incorporating information from the experimental design into a normalization method has the potential to yield more accurate results. To do this, we developed a new method, design-informed size factor estimation (disize), that uses a statistical model to jointly account for the biological signal defined by the design and the sample-specific batch effect. By separating the biological variation into its components, disize can more robustly estimate the batch effect. To validate our approach, we constructed a flexible simulation framework relying on a mechanistically justified data-generating process for RNA-seq data. Our benchmarks on both simulated and true RNA-seq data show that disize recovers the true size factors more accurately than existing methods, particularly in challenging scenarios with low counts or a high proportion of differentially expressed genes. This improved normalization yields more reliable downstream results in differential expression analysis.

Antimicrobial-resistant organisms (AMROs) impose a major burden on healthcare systems, yet routine surveillance cannot readily distinguish colonization imported at admission from transmission acquired within hospitals. This gap is especially consequential because both processes may vary substantially across wards, while asymptomatic carriage, incomplete testing, imperfect diagnostic sensitivity, and patient movement obscure the underlying transmission dynamics. To address this challenge, we developed a blockwise agent-based iterated filter (BAIF) for inference in a patient-level transmission model on a dynamic ward co-location network. The model tracks susceptible and colonized patients as they move across wards, represents unobserved colonization histories, and incorporates the recorded testing schedule and imperfect diagnostic sensitivity. BAIF uses blockwise likelihood evaluation and resampling to estimate ward-block-specific transmission rates and importation probabilities in this high-dimensional latent system. Synthetic experiments showed that BAIF recovered these parameters from partially observed outbreaks. We then applied the framework to hospitalization and microbiological surveillance data collected from 2012 to 2016 at an urban quaternary care hospital in New York City for four AMROs. Transmission and importation were highly heterogeneous across ward blocks. Elevated transmission was repeatedly concentrated in the same ward groups, whereas blocks with the highest importation varied by pathogen. By distinguishing importation-dominated from transmission-dominated ward blocks, the framework can inform more targeted surveillance and infection-control strategies. More broadly, BAIF provides an effective inference framework for high-dimensional, partially observed agent-based models on dynamic contact networks.

Controlled human infection models (CHIMs) are increasingly used in infectious disease research to study pathogen dynamics and evaluate interventions under controlled conditions. However, these studies are resource-intensive and involve ethical and safety constraints, making efficient study design critical. Dose-finding is a key early component in CHIMs, where the aim is to identify a challenge dose that achieves a target infection probability. Traditional rule-based designs are commonly used but can be inefficient, motivating the use of model-based adaptive approaches such as the Bayesian Continual Reassessment Method (CRM). Although CRM has been extensively studied and widely adopted in Phase I oncology trials for identifying the maximum tolerated dose of therapeutics, its application in CHIM settings remains limited, particularly when the endpoint of interest is infection. This tutorial provides step-by-step guidance for implementing a Bayesian CRM in dose-finding CHIMs, using an oropharyngeal Neisseria gonorrhoeae challenge as a motivating case study. The framework outlines key design components, including dose-grid specification, dose-response model, prior elicitation, Bayesian updating, decision rules, and stopping criteria, with particular emphasis on a clinically interpretable parameterisation. Trial operating characteristics are evaluated through simulation studies under multiple dose-response scenarios and prior-predictive analyses, and compared with a commonly used '3+3' type rule-based design. This work highlights the advantages of Bayesian model-based designs for dose-finding in CHIMs over classic rule-based designs and provides a structured, reproducible framework for implementing CRM, supporting their application in future CHIM studies.

Background: Descriptive mapping of intensive care unit (ICU) and high care unit (HCU) capacity across Japan's secondary medical areas (SMAs) characterizes where beds exist, but medical planning also requires answers to prospective questions: how likely is a capacity shortfall under demand surge, which assumptions drive that risk, and how much protection do inter-zone transfer arrangements provide. No openly available tool addresses these questions at the SMA level, the geographic unit at which Japanese medical plans are written. Methods: We developed MeshScope-Scenario, a probabilistic capacity-demand framework operating on the MeshScope-Region platform. For a selected SMA, observed inputs (notified ICU/HCU beds from the Hospital Bed Function Reports; resident population) are combined with four explicitly flagged assumption parameters - effective staffed-bed rate, concurrent severe-care demand per 100,000 population, surge multiplier, and net cross-boundary inflow - each with a user-specified distribution. A seeded Monte Carlo engine (deterministic reproduction under a fixed seed) estimates the distribution of bed shortfall; interventions are compared under common random numbers. Parameter dependence is introduced by a Gaussian copula with automatic positive-semidefinite correction; global sensitivity is quantified by Sobol first-order and total-order indices (Saltelli sampling, Jansen estimators) alongside a deterministic one-at-a-time tornado analysis. A two-zone extension transfers unmet demand to the nearest ICU-holding SMA using road-network travel times measured in MeshScope-Region, with a transfer time limit and an acceptance cap; because both zones share the same systemic draws, correlated exhaustion of donor capacity under surge ("shared-fate" risk) is represented structurally. A seasonal layer applies twelve monthly surge multipliers and reports the distribution of annual maximum shortfall and month-specific shortfall probabilities. The engine is a dependency-free pure-function module verified by 40 statistical tests. Results: The framework reproduces identical output under identical seed and input; a flat seasonal profile reproduces the non-seasonal model exactly; copula factorization error is below 1e-9; and 10,000 iterations across three intervention variants complete in approximately 50 ms in a standard browser, permitting fully interactive use. Applied to three archetypal SMAs from the observed FY2024 supply map (seed 42, 20,000 iterations, demand prior 5 per 100,000), an ICU-zero zone with a transfer partner 48 minutes away has shortfall probability 66.0% (P50 4.8, P90 20.7 beds); a median metropolitan zone, 32.7% (P90 5.9), with Sobol indices ranking demand density and surge dominant; a high-supply zone shows zero shortfall up to approximately 2.5x surge. For the ICU-zero zone, independent-donor reasoning credits the transfer arrangement with a 2.27-bed reduction in expected shortfall, of which shared-fate correlation removes 93%; the probability of severe shortfall under the arrangement equals that with no arrangement at all, while a committed pool of five donor beds (6% of donor effective supply) restores a 13-point reduction and more than halves the arrangement's correlation exposure. Conclusions: MeshScope-Scenario extends SMA-level capacity mapping from description to prospective risk assessment. All demand-side inputs are declared assumptions with adjustable distributions rather than estimates presented as fact; the framework's value is to make the consequences of those assumptions, and their interaction with observed supply, explicit, reproducible, and inspectable for planning deliberation.

In meta-analyses of continuous outcomes, the sample mean and standard deviation (SD) are essential for synthesizing effect sizes across studies. However, clinical studies frequently report alternative summary statistics, such as the median, quartiles, and range. To enable inclusion of such studies, various methods have been proposed to estimate the sample mean and SD from these reported summaries. We propose the Bayesian Order Statistics-based Estimator (BOSE), which leverages the joint likelihood of observed order statistics together with weakly informative priors to obtain the full posterior distribution for the mean and SD without relying on computationally intensive iterative procedures such as Markov chain Monte Carlo algorithms. Our numerical studies demonstrate that BOSE performs competitively with existing approaches in estimating the mean, while achieving superior performance for estimating the SD across all evaluated scenarios, particularly in small-sample settings. Under non-normal distributions including skewed, heavy-tailed, and bimodal settings with mild or moderate deviations from normality, BOSE remains robust and stable, whereas methods specifically designed for skewed distributions may become unstable or even inapplicable. Beyond point estimation, BOSE naturally provides empirically validated posterior credible intervals, enabling researchers to formally quantify uncertainty for study-level estimates and make reliable, evidence-based decisions in meta-analytic research synthesis. A publicly accessible web application implementing BOSE and competing methods is also provided to facilitate practical use in meta-analytic research.

MotivationParent of Origin Effects (POEs), where the effect of an an allele on a phenotype differs based on maternal or paternal inheritance implicated in growth, metabolism, and neurodevelopment. Traditional tests for POEs require family data to determine parental origins of transmitted alleles. Given that such studies are expensive and time consuming compared to genome-wide association studies (GWAS), tests that function absent inheritance information are highly desirable. We develop a method, based on community detection from machine learning, that infers POEs via a spectral decomposition, obtains confidence intervals via a non-parametric bootstrap, and safeguards against confounding by non POE sources of variation. We refer to our method as Parent of Origin Inference via Spectral Estimation (POISE). ResultsWe demonstrate that POISE is well-calibrated under both Gaussian and heavy-tailed noise in simulation studies, with improved robustness to true POEs compared to existing covariance-based tests. POISE provides per-trait effect estimates with bias-corrected bootstrap confidence intervals and incorporates an information-theoretic minimum detectable effect size that filters unreliable estimates, conferring robustness to covariance-deflating variance QTL. We then apply POISE to GWAS data from the UK Biobank using BMI, LDL cholesterol, and HDL cholesterol. POISE recovers established POE loci and identifies 134 additional variants at genes implicated in lipid metabolism, immune regulation, and growth. Availability and implementationThe code for this method in Python is available at https://github.com/bystrogenomics/POISE.

Causal mediation analysis is widely used to identify biological pathways linking exposures to outcomes, but most methods assume homogeneous mediation effects across individuals. In high-dimensional omics settings, this assumption can mask important heterogeneity driven by demographic, genetic, or environmental factors. We propose the M-high-learner, a flexible framework for detecting heterogeneous mediation effects with high-dimensional mediators. The method identifies mediators with subgroup-specific indirect effects while distinguishing them from null or homogeneous signals and controlling the type I error rate. It is computationally efficient, scalable, and yields interpretable sub-types. Simulation studies show that the proposed approach achieves high power while maintaining accurate error control. Applications to the Framingham Heart Study and the Multi-Ethnic Study of Atherosclerosis reveal that the mediation role of gene expression in sexs effect on high-density lipoprotein varies across subgroups defined by body mass index and age. Our framework provides a practical tool for uncovering heterogeneous biological mechanisms in high-dimensional genomic studies. Author SummaryBiological processes linking risk factors to disease often differ across individuals, but many existing methods assume these processes are the same for everyone. This can hide important differences between groups. We developed a powerful method to identify when these pathways vary across subgroups using large-scale molecular data. Our approach detects differences in how intermediate biological factors contribute to outcomes in populations defined by characteristics such as age and body mass index. Applying our method to population studies, we found that some biological pathways operate differently across groups, suggesting that key mechanisms may be missed when differences are ignored. Our work provides a tool to better understand how disease-related processes vary across individuals, which may support more targeted and personalized approaches to health research.

The incidence rate ratio R is the standard measure for comparing event rates in clinical trials and epidemiology. In vaccine trials, the vaccine efficacy is VE = 1 - R. When events are rare, the two arm counts are Poisson. The estimator of R is heteroskedastic: its sampling variance changes with the data. So no fixed-width interval covers correctly everywhere. The usual log-Wald interval is undefined at zero events and covers poorly at small counts. Early vaccine and drug-safety readouts fall in exactly this regime. We show that a single reparameterization collapses this bivariate problem to an effective one-parameter family with a quadratic variance function, whose variance-stabilizing transformation is 2 arcsinh(sqrt(R)). The reduction yields a closed-form confidence interval for R. Its two leading errors, a curvature bias and the variability of the estimated scale, each admit a closed-form correction with no tuning constants. In a Monte Carlo study of our seven arcsinh variants and five competitors, the +Curve+Stu variant covers within 0.002 of the nominal 0.95 for about 50 control and 5 treatment events. Its width is on par with the best competitor. It avoids the conservatism and zero-count breakdown of log-Wald and MOVER. For moderate counts, we recommend this interval; for sparser data, our Bar-Lev and Enis count-shift variant is more robust. The result is a ready-to-use, closed-form interval for the low-count regime. We illustrate it on early Covid-19 vaccine-efficacy readouts and provide reference implementations in R and Python.

Acute viral respiratory infections (ARVIs) are a major cause of hospitalization and death worldwide, yet randomized clinical trials in this setting face substantial challenges in selecting efficient and clinically meaningful primary endpoints. Mortality is often too infrequent to serve as a feasible primary endpoint. Several alternative approaches have been proposed, including ordinal scales, time-to-event endpoints, recovery-based composite outcomes, and longitudinal ordinal models. However, their comparative operating characteristics under realistic ARVI disease courses remain insufficiently understood. We describe a simulation study to compare the type I error and power of commonly used and recently proposed endpoints and analysis strategies for two-arm randomized trials in hospitalized participants with ARVIs. Data will be generated under several mechanisms designed to mimic plausible participant trajectories, including a latent Brownian motion process, a first-order ordinal Markov process, a latent recurrent-event process with frailty, and resampling from individual participant data from the ACTT-2 trial. Simulated outcomes will use 4-, 6-, and 8-level ordinal severity scales and will reflect moderately and severely ill populations, follow-up horizons of 28 or 60 days, varying treatment effects, and sample sizes. Methods to be compared include Markov ordinal state transition models, proportional-odds models at a fixed time point, days-to-recovery scale analyses, Cox models for time-to-event endpoints, logistic regression for binary endpoints, generalized pairwise comparisons for hierarchical composites, and t-tests for days alive and out of hospital. This study will provide a systematic comparison of endpoint definitions and analysis methods for ARVI trials under clinically motivated data-generating mechanisms. The results are intended to inform the selection of feasible, interpretable, and statistically efficient primary analysis strategies for future trials in viral respiratory disease.