Accurate estimates of microbial species abundances are needed to advance our understanding of the role that microbiomes play in human and environmental health. However, laboratory-constructed microbiomes demonstrate that intuitive estimators of microbial relative abundances are biased. To address this, we propose a method to estimate relative abundances, species detection effects, and/or cross-sample contamination in microbiome experiments. We show that certain experimental designs result in identifiable model parameters, and present consistent estimators and asymptotically valid inference procedures that are robust to misspecification of the working likelihood. Notably, our procedure can estimate relative abundances on the boundary of the simplex. We demonstrate the utility of the method for comparing experimental protocols, removing cross-sample contamination, and estimating species' detectability.

In survival analysis, integrating multiple data sources holds significant potential to increase event counts, improve statistical efficiency, and enhance scientific discovery. However, challenges arise when external datasets differ in data forms, distributions, and covariate availability. We propose a novel learning framework that strengthens statistical inference of internal Cox regression coefficients through efficient data integration. Our framework is robust integration and accommodates multifaceted data heterogeneity through transformation, working models, penalized approach, and weighting that adaptively selects homogeneous components. The resulting estimators are consistent, asymptotically normal, more efficient than internal-only estimators, and robust to working model misspecification and data heterogeneity. Simulation studies confirm our framework's favorable properties. We applied our method to study dementia risk among black individuals in the Religious Orders Study and the Rush Memory and Aging Project, integrating information from external cohorts, including the Alzheimer's Disease Neuroimaging Initiative and the National Alzheimer's Coordinating Center. The analysis identified key risk factors with improved efficiency and demonstrated clinical relevance.

Estimating optimal dynamic treatment regimes (DTRs) from observational data is often complicated by nonignorable missing covariates arising from informative monitoring in clinical practice. A recent weighted Q-learning approach addresses such nonignorable missingness but relies on parametric Q-function models. In multistage settings, misspecification of Q-function models can propagate backwards through pseudo-outcomes and accumulate bias, both directly through Q-function estimation and indirectly through biased estimation of missingness propensities, raising robustness concerns. Motivated by these limitations, we develop robust direct-search estimators of optimal single-stage and multistage treatment rules with nonignorable missing covariates. For single-stage rules, under a future-independent missingness assumption, we construct robust estimators of treatment regime values using covariate balancing weights, augmented with nonparametrically estimated Q-functions. For DTRs, we extend the augmented value estimator by incorporating pseudo-outcomes with inverse missingness propensity weighting, where missingness propensities are modeled semiparametrically using nonresponse instrumental variables. Simulation studies demonstrate that the proposed estimators are more robust to model misspecification and contamination by invalid instrumental variables than weighted Q-learning. An application to electronic medical record data illustrates improved estimation of early fluid resuscitation strategies for sepsis patients when nonignorable missing hemodynamic variables are utilized.

Pocket depth (PD) is a widely used biomarker for diagnosing risk of periodontal disease (PrD). However, PD typically exhibits skewness and heavy-tailedness, and its relationship with clinical risk factors is often nonlinear. Motivated by PrD studies, this paper develops a robust single-index modal regression framework for analyzing skewed and heavy-tailed data. Our method has the following novel features: (i) a flexible 2-piece scale Student-$ \boldsymbol{t} $ error distribution that generalizes both normal and 2-piece scale normal distributions; (ii) a neural network with guaranteed monotonicity constraints to estimate the unknown single-index function; and (iii) theoretical support, including model identifiability and a universal approximation theorem. Our single-index model combines the flexibility of neural networks and the 2-piece scaled Student-$ \boldsymbol{t} $ distribution, delivering robust mode-based estimation that is resistant to outliers, while retaining clinical interpretability through parametric index coefficients. We demonstrate the performance of our method through simulation studies and an application to PrD electronic health records obtained from the HealthPartners Institute of Minnesota. The proposed methodology is implemented in the R package DNNSIM.

The identification of patient subgroups with comparable event-risk dynamics plays a key role in supporting informed decision-making in clinical research. In such settings, it is important to account for the inherent dependence that arises when individuals are nested within higher-level units, such as hospitals. Existing survival models account for group-level heterogeneity through frailty terms but do not uncover latent patient subgroups, while most clustering methods ignore hierarchical structure and are not estimated jointly with survival outcomes. In this work, we introduce a new framework that simultaneously performs patient clustering and shared-frailty survival modeling through a penalized likelihood approach. The proposed methodology adaptively learns a patient-to-patient similarity matrix via a modified version of spectral clustering, enabling cluster formation directly from estimated risk profiles while accounting for group membership. A simulation study highlights the proposed model's ability to recover latent clusters and to correctly estimate hazard parameters. We apply our method to a large cohort of heart-failure patients hospitalized with COVID-19 between 2020 and 2021 in the Lombardy region (Italy), identifying clinically meaningful subgroups characterized by distinct risk profiles and highlighting the role of respiratory comorbidities and hospital-level variability in shaping mortality outcomes. This framework provides a flexible and interpretable tool for risk-based patient stratification in hierarchical data settings. Accompanying software is available at https://github.com/alessandragni/HazClust.

Time-lag bias occurs when studies with larger or statistically significant effects appear in print sooner than those with smaller or null effects, which can inflate early meta-analytic estimates and mislead decision makers. Existing diagnostics typically rely on cumulative meta-analysis or meta-regression of effects on publication time. These approaches are vulnerable to repeated-testing inflation and loss of power when temporal trends are nonlinear. We propose a family of flexible nonparametric statistics that summarize standardized pairwise differences between early and later studies, each emphasizing different magnitudes of temporal contrast, and a hybrid test that adaptively combines evidence across these differences via permutation. The procedure controls type I error under exchangeability of publication order and is sensitive to diverse temporal patterns, including linear trends, early outliers, and oscillations in effect sizes over time. Simulations across 9 scenarios, with and without heterogeneity, show that the hybrid test maintains the nominal error rate and improves statistical power relative to linear meta-regression when temporal trends are nonlinear. In 2 clinical meta-analyses, our method detects pronounced time-lag patterns that standard regression misses. We provide guidance for practice and discuss practical considerations for detecting and interpreting time-lag bias in meta-analyses.

Gene expression is a primary modality being studied to differentiate between biological cells. Contemporary single-cell studies simultaneously measure genome-wide transcription levels for thousands of individual cells in a single experiment. While the characterization of cell population differences has often occurred through differential gene expression analysis, tiny effect sizes become statistically significant when thousands of cells are available for each population, compromising biological interpretation. Moreover, these large studies have spurred the development of methods to infer gene regulatory networks (GRNs) directly from the data, and GRN databases are becoming more comprehensive. In this work, we propose a statistical model for gene expression measures and an inference method that leverage GRNs to deconvolve transcription factor (TF) activity from gene expression, by probabilistically assigning mRNA molecules to TFs. This shifts the paradigm from investigating gene expression differences to regulatory differences at the level of TF activity, aiding interpretation and allowing prioritization of a limited number of TFs responsible for significant contributions to the observed gene expression differences. The inferred TF activities result in intuitive prioritization of TFs in terms of the (difference in) estimated number of molecules they produce, in contrast to other widely used methods relying on arbitrary enrichment scores. Our model allows the incorporation of prior information on the regulatory potential between each TF and target gene and is able to deal with both repressing and activating interactions. We compare our approach to other TF activity estimation methods using two simulation experiments and two case studies. Single-cell RNA-sequencing; TF activity; bioinformatics; GRN.

Large-scale neuroimaging studies often collect data from multiple scanners across different sites, where variations in scanners, scanning procedures, and other conditions across sites can introduce artificial site effects. These effects may bias brain connectivity measures, such as functional connectivity, which quantify functional network organization derived from functional magnetic resonance imaging. How to leverage high-dimensional network structures to effectively mitigate site effects has yet to be addressed. In this paper, we propose Sparse LAtent Covariate-driven Connectome (SLACC) factorization, a multivariate method that explicitly parameterizes covariate effects in latent subject scores corresponding to sparse rank-1 latent patterns derived from brain connectivity. The proposed method identifies localized site-driven variability within and across brain networks, enabling targeted correction. We develop a penalized Expectation-Maximization algorithm for parameter estimation, incorporating the Bayesian Information Criterion to guide optimization. Extensive simulations validate SLACC's robustness in recovering the true parameters and underlying connectivity patterns. Applied to the Autism Brain Imaging Data Exchange dataset, SLACC demonstrates its ability to reduce site effects.

This paper introduces a novel decomposition framework to explain heterogeneity in causal effects observed across different studies, considering both observational and randomized settings. We present a formal decomposition of between-study heterogeneity, identifying sources of variability in treatment effects across studies. The proposed methodology allows for robust estimation of causal parameters under various assumptions, addressing differences in pre-treatment covariate distributions, mediating variables, and the outcome mechanism. Our approach is validated through a simulation study and applied to data from the Moving to Opportunity (MTO) study, demonstrating its practical relevance. This work contributes to the broader understanding of causal inference in multi-study environments, with potential applications in evidence synthesis and policy-making.

Advances in cellular imaging technologies, especially those based on fluorescence in situ hybridization (FISH), now allow detailed visualization of the spatial organization of human or bacterial cells. Quantifying this spatial organization is crucial for understanding the function of multicellular tissues or biofilms, with implications for human health and disease. To address the need for better methods to achieve such quantification, we propose a flexible multivariate point process model that characterizes and estimates complex spatial interactions among multiple cell types. The proposed Bayesian framework is appealing due to its unified estimation process and the ability to directly quantify uncertainty in key estimates of interest, such as those of inter-type correlation and the proportion of variance due to inter-type relationships. To ensure stable and interpretable estimation, we consider shrinkage priors for coefficients associated with latent processes that induce dependencies among point patterns. Model selection and comparison are conducted using a deviance information criterion designed for models with latent variables, providing a practical criterion for balancing model fit and complexity. Furthermore, we use a Bayesian hierarchical pooling model to synthesize image-specific posterior summaries, allowing inference at both the global- (across subjects) and subject-specific levels. An R package, mspatPPM, implements an efficient computational scheme based on Hamiltonian Monte Carlo and adaptive Metropolis-Hastings algorithms. Numerical studies evaluate the practical performance of the proposed framework for model selection and for estimating quantities that characterize the multivariate spatial distribution of cell types. We apply the proposed method to microbial biofilm image data from the human tongue dorsum and find that specific taxon pairs, such as Streptococcus mitis-Streptococcus salivarius and S. mitis-Veillonella, exhibit strong positive spatial correlations, while others, such as Actinomyces-Rothia, show slight negative correlations. For most of the taxa, a substantial portion of spatial variance can be attributed to inter-taxon relationships.

Egocentric-network randomized trials (ENRTs) are increasingly used to estimate causal effects under interference when measuring complete sociocentric network data is infeasible. ENRTs rely on egocentric network sampling, where a set of egos is first sampled, and each ego recruits a subset of its neighbors as alters. Treatments are then randomized across egos. While the observed ego-networks are disjoint by design, the underlying population network may contain edges connecting them, leading to contamination. Under a design-based framework, we show that the Horvitz-Thompson estimators of direct and indirect effects are biased whenever contamination is present. To address this, we derive bias-corrected estimators and propose a novel sensitivity analysis framework based on sensitivity parameters representing the probability or expected number of missing edges. This framework is implemented via both grid sensitivity analysis and probabilistic bias analysis, providing researchers with a flexible tool to assess the robustness of the causal estimators to contamination. We apply our methodology to the HIV Prevention Trials Network 037 study, finding that ignoring contamination may lead to underestimation of indirect effects and overestimation of direct effects.

Inflammatory Bowel Disease (IBD), including Crohn's Disease (CD) and Ulcerative Colitis (UC), presents significant public health challenges due to its complex etiology. Motivated by the IBD study of the Integrative Human Microbiome Project, our objective is to identify microbial pathways that distinguish between CD, UC, and non-IBD over time. Most current research relies on simplistic analyses that examine 1 variable or time point at a time, or address binary classification problems, limiting our understanding of the dynamic interactions within the microbiome over time. To address these limitations, we develop a novel functional data analysis approach for discriminant analysis of multivariate functional data that can effectively handle multiple high-dimensional predictors, sparse time points, and categorical outcomes. Our method seeks linear combinations of functions (ie discriminant functions) that maximize separation between 2 or more classes over time. We impose a sparsity-inducing penalty when estimating the discriminant functions, allowing us to identify relevant discriminating variables over time. Applications of our method to the motivating data identified microbial features related to mucin degradation, amino acid metabolism, and peptidoglycan recognition, which are implicated in the progression and development of IBD. Furthermore, our method highlighted the role of multiple vitamin B deficiencies in the context of IBD. By moving beyond traditional analytical frameworks, our innovative approach holds the potential for uncovering clinically meaningful discoveries in IBD research.

Quantifying the causal effect of a treatment on a disease is a crucial task in medical science for the administration of effective therapies. Typically, such causal effects are inferred from multivariate data that are collected on patients and recorded in the form of categorical variables, including risk factors involved in disease progression, treatment assignments, and disease status. This feature motivates an approach to causal inference based on categorical Directed Acyclic Graphs (DAGs), which provide an effective framework for causal reasoning in complex multivariate settings. In this context, traditional DAG-based methods assume population homogeneity and accordingly attribute a unique causal effect to all subjects. However, this assumption is often unrealistic in clinical contexts, since patients may exhibit heterogeneous characteristics, possibly linked to unmeasured features. To address this issue, we propose a Bayesian nonparametric methodology based on a Dirichlet Process mixture of categorical DAGs, which allows treatment effects to vary across individuals because of underlying clustering structures in the data. We develop a Markov chain Monte Carlo algorithm for Bayesian posterior inference and evaluate our methodology through simulation studies. We then analyze patients affected by HER2+ breast cancer undergoing therapies that may cause cardiotoxic side effects. Importantly, our findings show that approaches neglecting population heterogeneity may produce biased results, since they can over- or under-estimate the risk of cardiotoxicity across patients.

In non-small cell lung cancer (NSCLC) clinical trials, tumor burden (TB) is a key longitudinal biomarker for assessing treatment effects. Typically, standard-of-care (SOC) therapies and some novel interventions initially decrease TB; however, many patients subsequently experience an increase-indicating disease progression-while others show a continuous decline. In patients with an eventual TB increase, the change point marks the onset of progression and must occur before the time of the event. To capture these dynamics, we propose a cure-rate joint model that integrates time-to-event and longitudinal TB data and distinguishes change-point and non-change-point trajectory groups. For the change-point group, our approach flexibly estimates an individualized change point by leveraging time-to-event information. We use a Monte Carlo Expectation-Maximization (MCEM) algorithm for efficient parameter estimation. Simulation studies show that our model can recover diverse disease progression patterns and yield robust marginal TB outcome estimates while accommodating censoring complexity. When applied to a Phase 3 NSCLC trial comparing cemiplimab monotherapy to SOC, the treatment group demonstrates delayed tumor regrowth and consistently lower TB over time, highlighting the clinical utility of our approach. The implementation code is publicly available on https://github.com/quyixiang/JoCuR.

Estimating treatment effects with electronic health record data is complicated by the lack of validated outcome measures collected under standard protocols. Typically, outcomes can only be approximated or inferred using putative rules or phenotyping models trained on small subsamples where validated outcomes are available. But biases in the inferred outcomes can introduce biases in downstream treatment effect estimates. To address this issue, we develop a semisupervised method to calibrate inferred outcomes for treatment effect estimation when a subsample is labeled at random with validated outcomes. The calibration ensures that the subsequent treatment effect estimator remains consistent despite errors in the inferred outcomes. This problem can be viewed to be analogous to that of estimating mean outcomes in a longitudinal study subject to monotone missingness, and we demonstrate connections with existing augmented inverse probability weighting estimators. The proposed estimator is multiply robust and locally semiparametric efficient. The finite sample performance is assessed through simulations. We also find that the estimator can achieve efficiency gains in finite samples in the presence of extreme labeling propensity scores owing to an effective normalization of implicit augmentation terms. The method is illustrated through an example analysis evaluating the comparative effectiveness of 2 anti-TNF therapies on disease activity status in patients with rheumatoid arthritis.