Biostatistics最新文献

{"title":"Sparse covariate-driven factorization of high-dimensional brain connectivity with application to site effect correction.","authors":"Rongqian Zhang, Elena Tuzhilina, Jun Young Park","doi":"10.1093/biostatistics/kxag034","DOIUrl":"10.1093/biostatistics/kxag034","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13544973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal estimands and identification of time-varying effects in nonstationary time series from N-of-1 mobile device data.","authors":"Xiaoxuan Cai, Li Zeng, Charlotte Fowler, Lisa Dixon, Dost Ongur, Justin T Baker, Jukka-Pekka Onnela, Linda Valeri","doi":"10.1093/biostatistics/kxag014","DOIUrl":"10.1093/biostatistics/kxag014","url":null,"abstract":"<p><p>Mobile technology (mobile phones and wearable devices) generates continuous data streams encompassing outcomes, exposures, and covariates, presented as intensive longitudinal or multivariate time series data. The high frequency of measurements enables granular and dynamic evaluation of treatment effect, revealing their persistence and accumulation over time. Existing methods predominantly focus on the contemporaneous effect, temporal-average, or population-average effects, assuming stationarity or invariance of treatment effects over time, which are inadequate both conceptually and statistically to capture dynamic treatment effects in personalized mobile health data. We here propose new causal estimands for multivariate time series in N-of-1 studies. These estimands summarize how time-varying exposures impact outcomes in both short- and long-term. We propose identifiability assumptions and a g-formula estimator that accounts for exposure-outcome and outcome-covariate feedback. The g-formula employs a state space model framework innovatively to accommodate time-varying behavior of treatment effects in nonstationary time series. We apply the proposed method to a multiyear smartphone observational study of bipolar patients and estimate the dynamic effect of phone-based communication on mood of patients with bipolar disorder in an N-of-1 setting. Our approach reveals substantial heterogeneity in treatment effects over time and across individuals. A simulation-based strategy is also proposed for the development of a short-term, dynamic, and personalized treatment recommendation based on patient's past information, in combination with a novel positivity diagnostics plot, validating proper causal inference in time series data.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148563786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disentangling regional heterogeneity in social determinants-cardiovascular disease associations in the US: a high-dimensional county clustering approach.","authors":"Fangzhi Luo, Heejung Son, Yewen Chen, Yanfang Su, Donglan S Zhang, Ye Shen","doi":"10.1093/biostatistics/kxag042","DOIUrl":"https://doi.org/10.1093/biostatistics/kxag042","url":null,"abstract":"<p><p>Social determinants of health (SDOH) are key drivers of cardiovascular disease (CVD), but their relationships differ substantially across regions, necessitating the design of locally tailored intervention strategies. In this work, we propose a novel clustering approach to partition US counties into different clusters based on similarities in their SDOH-CVD associations, providing a data-driven regional partition to inform region-specific interventions. To enhance the interpretability of the clustering result, we adopt the SDOH framework from the Agency for Healthcare Research and Quality to group SDOH variables into 5 domains, and build on this to develop an SDOH-domain-aware regularization (SDAR), which identifies the most influential variables within each SDOH domain tailored for each cluster. By embedding SDAR into the clustering process via a regularized expectation-maximization algorithm, our approach enables simultaneous clustering, parameter estimation, and variable selection, thereby mitigating error propagation across stages. We validate the method through theoretical developments and simulations. Applied to a national longitudinal dataset of US county-level SDOH and CVD mortality, our approach discovered 7 distinct clusters, each characterized by a unique set of SDOH drivers spanning multiple SDOH domains. Notably, 2 of these clusters exhibited elevated CVD mortality and inverse SDOH-CVD associations compared to the other 5. These findings reveal complex regional heterogeneity in SDOH-CVD associations, yielding insights to guide multi-domain, region-specific strategies for CVD management.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A doubly robust framework for addressing outcome-dependent selection bias in multi-cohort EHR studies.","authors":"Ritoban Kundu, Xu Shi, Michael Kleinsasser, Lars G Fritsche, Maxwell Salvatore, Bhramar Mukherjee","doi":"10.1093/biostatistics/kxag001","DOIUrl":"10.1093/biostatistics/kxag001","url":null,"abstract":"<p><p>Selection bias can hinder accurate estimation of association parameters in binary disease risk models using non-probability samples like electronic health records (EHRs). The issue is compounded when participants are recruited from multiple clinics/centers with varying selection mechanisms that may depend on the disease/outcome of interest. Traditional inverse-probability-weighted (IPW) methods, based on constructed parametric selection models, often struggle with misspecifications when selection mechanisms vary across cohorts. This paper introduces a new Joint Augmented Inverse Probability Weighted (JAIPW) method, which integrates individual-level data from multiple cohorts collected under potentially outcome-dependent selection mechanisms, with data from an external probability sample. JAIPW offers double robustness by incorporating a flexible auxiliary score model to address potential misspecifications in the selection models. We outline the asymptotic properties of the JAIPW estimator, and our simulations reveal that JAIPW achieves up to 6 times lower relative bias and 5 times lower root mean square error (RMSE) compared to the best performing joint IPW methods under scenarios with misspecified selection models. Applying JAIPW to the Michigan Genomics Initiative (MGI), a multi-clinic EHR-linked biobank, combined with external national probability samples, resulted in cancer-sex association estimates closely aligned with national benchmark estimates. We also analyzed the association between cancer and polygenic risk scores (PRS) in MGI to illustrate a situation where the exposure variable is not measured in the external probability sample.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13043170/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147596592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scalable inference for spatial generalized mixed models for use in multiplexed imaging analysis.","authors":"Caitlin Ward, Brenna C Novotny, Raymond M Moore, Ruifeng Guo, Alexey Leontovich, Wendy K Nevala, Zachary Fogarty, Svetomir N Markovic, Chen Wang","doi":"10.1093/biostatistics/kxag037","DOIUrl":"10.1093/biostatistics/kxag037","url":null,"abstract":"<p><p>Understanding how cells interact to coordinate tissue function and respond to disease remains a central challenge in biology. Recently developed multiplex bioimaging technologies [multiplex immunofluorescence (MxIF)] can quantify multiple protein markers at a single-cell resolution in spatially preserved tissue, offering the opportunity to understand how cellular organization relates to disease mechanisms and treatment response. Spatial generalized linear mixed models for cell co-localization offer a flexible framework for these analyses, accommodating cell-, image-, or subject-level predictors while accounting for correlation arising from repeated cells per image, repeated images per subject, and spatial dependence among nearby cells. However, as MxIF data typically have hundreds of thousands to millions of cells per study, fitting the full spatial model is computationally infeasible due to the large, dense, spatial covariance matrix. We present a scalable inference strategy that uses parallelizable eigen-decomposition of an empirically estimated spatial correlation matrix to approximate the full spatial model. This approach enables fast estimation of fixed effects of interest without requiring formal estimation of spatial parameters or surface prediction. In simulations, our proposed method could be fitted to large datasets ($ sim $ 285,000 observations) in under a minute, with runtime linearly increasing by only $ sim $ 7 s per 50,000 additional observations. This scalability makes cell-level modeling in the presence of spatial correlation feasible for real-world MxIF datasets, as we demonstrate through applications to metastatic melanoma and high-grade serous ovarian carcinoma.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13658279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149169610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust estimation of optimal dynamic treatment regimes with nonignorable missing covariates.","authors":"Jian Sun, Bo Fu, Li Su","doi":"10.1093/biostatistics/kxag041","DOIUrl":"https://doi.org/10.1093/biostatistics/kxag041","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149129274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical modeling and inference for Jack's Alzheimer's disease biomarker cascade with practical and computational considerations.","authors":"Zhuojun Tang, Xinyi Zhang, Yuxin Zhu, Zheyu Wang","doi":"10.1093/biostatistics/kxag038","DOIUrl":"https://doi.org/10.1093/biostatistics/kxag038","url":null,"abstract":"<p><p>Alzheimer's Disease (AD) research increasingly recognizes the importance of characterizing the joint and relative dynamics of biomarker trajectories during the preclinical phase. Jack's model stands as the most well-known theoretical framework of these processes. While it offers conceptual guidance on modeling, it is not a directly implementable model for observed data. Key challenges include: (i) biomarker measurements are often noisy, depend on individual characteristics, and often exhibit complex temporal and cross-sectional correlations; (ii) data are sampled sparsely and may cover only a limited window of disease progression; and (iii) the temporal scale of the disease may not be synchronized across individuals. To address these challenges, we introduce a multivariate (for the multiple biomarkers), sparse (for the longitudinal sampling mechanism), nonlinear (for the hypothesized S-shape of biomarker evolution) latent variable model that accounts for complex correlation structures and individual factors. Crucially, we identify and address substantial practical and computational issues due to the noisy, sparse, and limited longitudinal data. Our contribution includes using a penalized likelihood to address estimates being close to the boundary due to degenerate cases, implementing a Gaussian Quadrature Newton-Raphson algorithm for efficient estimation under flexible correlation structures, and proposing a practical solution for potentially incomplete biomarker trajectories. We evaluate the finite-sample performance of the proposed method through simulations designed to mimic real-world data. We then apply the model to 2 longitudinal AD cohorts, BIOCARD and ADNI, to estimate biomarker trajectories across imaging, cerebrospinal fluid, and cognitive domains. These applications provide insights into the temporal dynamics of AD biomarkers, supporting improvements in preclinical diagnosis and guiding future research.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.","authors":"Yuying Lu, Tian Gu, Rui Duan","doi":"10.1093/biostatistics/kxag011","DOIUrl":"10.1093/biostatistics/kxag011","url":null,"abstract":"<p><p>To address the challenges in modeling time-to-event outcomes in small-sample settings, we propose a novel transfer learning approach, termed CoxTL, based on the widely used Cox proportional hazards model, accounting for potential covariate and concept shifts between source and target datasets. CoxTL utilizes a combination of density ratio weighting and importance weighting techniques to address multi-level data heterogeneity, including covariate and coefficient shifts between source and target datasets. Additionally, it accounts for potential model misspecification, ensuring robustness across a wide range of settings. We assess the performance of CoxTL through extensive simulation studies, considering data under various types of distributional shifts. In addition, we apply CoxTL to predict End-Stage Renal Disease (ESRD) in the Hispanic population using electronic health record-derived features from the All of Us Research Program. Data from non-Hispanic White and non-Hispanic Black populations are leveraged as source cohorts. Model performance is evaluated using the C-index and Integrated Brier Score (IBS). In simulation studies, CoxTL demonstrates higher predictive accuracy, particularly in scenarios involving multi-level heterogeneity between target and source datasets. In other scenarios, CoxTL performs comparably to alternative methods specifically designed to address only a single type of distributional shift. For predicting the 2-year risk of ESRD in the Hispanic population, CoxTL achieves an increase in the C-index of up to 6.76% compared to the model trained exclusively on target data. Furthermore, it demonstrates up to a 17.94% increase in the C-index compared to the state-of-the-art transfer learning method based on Cox model. The proposed method effectively utilizes source data to enhance time-to-event predictions in target populations with limited samples. Its ability to handle various sources and levels of data heterogeneity ensures robustness, making it particularly well-suited for real-world applications involving target populations with small sample sizes, where traditional Cox models often struggle.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231514/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148152326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A divide and conquer strategy for recapitulating whole genome 3D structure using Hi-C data.","authors":"Jincheol Park, Meng Wang, Emerson Webb, Victor Jin, Shili Lin","doi":"10.1093/biostatistics/kxag021","DOIUrl":"10.1093/biostatistics/kxag021","url":null,"abstract":"<p><p>The three dimensional (3D) spatial organization of the genome is closely linked to biological functions and can be captured by Hi-C assays through interrogating genome-wide chromatin interactions. Methodologies for inferring 3D structures from Hi-C data summarized as a two-dimensional (2D) contact matrix can be broadly placed within the paradigms of optimization-based and sampling-based. Many optimization-based methods are capable of constructing whole genome 3D structures but do not account for spatial dependency in the 2D data matrix nor cell heterogeneity in bulk Hi-C data, which provide an average over millions of cells. Sampling-based methods, on the other hand, are probabilistic model-based and can account for not only dependency, heterogeneity, but also other features inherent in Hi-C data, such as over-dispersion and sparsity. However, whole-genome 3D structure recapitulation is too computationally expensive for sampling-based methods, while chromosome-by-chromosome strategies for sampling-based methods ignore important information on inter-chromosomal contacts. To address these issues, we propose the truncated Random effect EXpression-cut and paste (tREX-cap) method, which applies the tREX model within a divide and conquer strategy. The resulting method inherits the good data-feature-cognizant properties of tREX and, in the meantime, can efficiently infer the whole genome 3D structure. We demonstrate the performance of tREX-cap through an extensive simulation study and analyses of a Hi-C lymphoblastoid dataset and a Hi-C IMR90 dataset.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13379638/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148474521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk estimation and dynamic prediction using discrete-time joint models for longitudinal and multistate data with interval and state censoring.","authors":"Lu You, Falastin Salami, Carina Törn, Åke Lernmark, Kendra Vehik, Xiang Liu, Peihua Qiu, Roy Tamura","doi":"10.1093/biostatistics/kxag018","DOIUrl":"10.1093/biostatistics/kxag018","url":null,"abstract":"<p><p>This paper presents a joint model of multivariate longitudinal data and multistate data with application to modeling and predicting autoantibody development in The Environmental Determinants of Diabetes in the Young (TEDDY) study. The model quantifies the risks of state transitions based on observed time-varying and non-time-varying risk factors. Based on the estimated model, a dynamic prediction approach is suggested to predict future state occupation probabilities using historical data. The proposed method can handle uncertainties in the observed data, due to measurement errors in the observed longitudinal data and interval censoring or missing information in the observed multistate data. For evaluating the predictions by the proposed approach, some performance metrics and their estimation are discussed. The proposed method is evaluated by some simulation studies. It is discussed in detail how this method can be used in analyzing the TEDDY data by properly handling the missing information and predicting future disease status using the proposed dynamic prediction algorithm.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13337610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148400170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}