Statistics in Biosciences最新文献

{"title":"Contrastive Latent Functional Model.","authors":"Yanlong Bai, Danh V Nguyen, Donatello Telesca, Abigail Dickinson, Shafali Jeste, Esra Kürüm, Damla Şentürk","doi":"10.1007/s12561-026-09524-w","DOIUrl":"10.1007/s12561-026-09524-w","url":null,"abstract":"<p><p>Functional Principal Component Analysis (FPCA) is a popular tool for representing functional data in lower-dimensions, along highly-interpretable components depicting directions of variation in the data across time. We extend FPCA to contrastive settings involving multiple groups with a focus on time-dynamic components which are shared across samples versus those that are unique to a particular sample. The proposed Contrastive Latent Functional Model (cLFM) integrates FPCA with contrastive learning principles to capture common directions of variation across datasets while isolating distinct features specific to each sample. The model employs a computationally efficient Expectation-Maximization (EM) algorithm for parameter estimation, ensuring orthogonality between shared and unique functional spaces. Simulation studies demonstrate the efficacy of the proposed method across different scenarios of shared and unique variation, varying sample size and error variance. Applied to neurodevelopmental and clinical datasets, including EEG studies and longitudinal studies of kidney function, cLFM reveals novel insights into group differences between autism vs neurotypical development and among mild to severe albuminuria subgroups of chronic kidney disease patients. Bridging contrastive analysis with functional data, this framework advances the capacity to disentangle complex variation patterns in multi-group functional studies in biomedical research.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2026-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13449598/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148698088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ReFIT: Federated Transfer Learning for Sequential Prediction and Uncertainty Quantification Using Streaming EHR Data.","authors":"Yuying Lu, Lan Luo, Tian Gu","doi":"10.1007/s12561-026-09525-9","DOIUrl":"10.1007/s12561-026-09525-9","url":null,"abstract":"<p><p>Modern biomedical data are increasingly collected across multiple institutions and time periods, creating opportunities for improved statistical inference through knowledge transfer, but also posing challenges for privacy, scalability, and distributional heterogeneity. We propose a Renewable Federated Incremental Transfer framework, termed ReFIT, for sequentially integrating information from streaming source datasets to improve model estimation and prediction in a target population with limited samples. ReFIT builds upon a density ratio model to account for covariate shift between the source and target populations and employs a renewable updating strategy that allows model parameters to be incrementally refined as new source data become available, using only summary-level information from prior sources. This framework ensures privacy preservation and computational efficiency while adapting to evolving data environments. Beyond improving predictive performance, ReFIT also quantifies predictive uncertainty within a conformal prediction framework, yielding valid prediction intervals that adapt as new information accumulates. Extensive simulation studies demonstrate that ReFIT achieves higher predictive accuracy and better uncertainty quantification than models trained on target or source data alone. The method remains robust under nonlinear model misspecification and varying degrees of source-target shift. Moreover, as ReFIT incrementally integrates additional source data, the conformal prediction intervals become progressively narrower without sacrificing coverage, evidencing improved statistical efficiency with growing information. In an electronic health record application for breast cancer prediction, ReFIT substantially improves prediction for the Hispanic population by sequentially leveraging information from non-Hispanic White patients collected over multiple time periods. These results highlight the potential of ReFIT as a general and practical framework for privacy-preserving, adaptive, and scalable learning from distributed and periodically updated biomedical data.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2026-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13344382/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional Varying-Index Coefficients Model for Dynamic Synergistic Gene-Environment Interactions.","authors":"Jingyi Zhang, Xu Liu, Honglang Wang, Yuehua Cui","doi":"10.1007/s12561-024-09472-3","DOIUrl":"10.1007/s12561-024-09472-3","url":null,"abstract":"<p><p>Human complex diseases are affected by both genetic and environmental factors. When multiple environmental risk factors are present, the interaction effect between a gene and the environmental mixture can be larger than the addition of individual interactions, resulting in the so-called synergistic gene-environment (G×E) interactions. Existing literature has shown the power of synergistic gene-environment interaction analysis with cross-sectional traits. In this work, we propose a functional varying index coefficient model for longitudinal traits together with multiple longitudinal environmental risk factors and assess how the genetic effects on a longitudinal disease trait are nonlinearly modified by a mixture of environmental influences. We derive an estimation procedure for the nonparametric functional varying index coefficients under the quadratic inference function and penalized spline framework. We evaluate some theoretical properties such as estimation consistency and asymptotic normality of the estimates. We further propose a hypothesis testing procedure to assess the significance of the synergistic G×E effect. The performance of the estimation and testing procedure is evaluated through Monte Carlo simulation studies. Finally, the utility of the method is illustrated by a real dataset from a pain sensitivity study in which SNP effects are nonlinearly modulated by a mixture of drug dosages and other environmental variables to affect patients' blood pressure and heart rate.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"18 ","pages":"111-130"},"PeriodicalIF":0.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13410927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148621896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of Trials with Composite Endpoints with the R Package CompAREdesign.","authors":"Jordi Cortés-Martínez, Marta Bofill-Roig, Guadalupe Gómez-Melis","doi":"10.1007/s12561-025-09488-3","DOIUrl":"10.1007/s12561-025-09488-3","url":null,"abstract":"<p><p>Composite endpoints are frequently used as primary endpoints in clinical trials, especially those involving time-to-event data. Designing such trials poses significant challenges due to the frequent violation of the proportional hazards assumption when composite endpoints are used, even if the assumption holds true for individual components. Consequently, the conventional formulae for sample size calculation no longer apply. This paper introduces the R package CompAREdesign which incorporates novel methodologies developed by the authors to compute key trial design elements, including sample size and effect sizes, based on information of composite endpoint components. Additionally, the package offers features for designing trials with binary composite endpoints, and functions to simulate trials under a wide range of scenarios. While several R packages exist for analyzing trials with composite endpoints, CompAREdesign package is, to our knowledge, the first package specifically tailored to the design phase of such studies. CompAREdesign is available on CRAN.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"18 2","pages":"395-417"},"PeriodicalIF":0.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13233987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148200476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed quantile regression for big data with missing covariates.","authors":"Ye Fan, Hua Zhou, Jin Zhou, Tiejun Tong, Nan Lin","doi":"10.1007/s12561-026-09528-6","DOIUrl":"10.1007/s12561-026-09528-6","url":null,"abstract":"<p><p>Data missing caused by non-response or drop-out appears routinely in modern medical studies. With the rapid development of information technology, medical data are becoming more and more massive in volume, and thus usually require distributed analysis in real-world applications, especially for cases where the data are collected from multiple centers or contain patient privacy. In this paper, we focus on developing efficient distributed algorithms to support quantile regression analysis in missing big data, which provides a powerful tool to model the skew and heterogeneous medical samples in reality, but currently remains a challenging issue. We employ the weighted quantile regression (WQR) technique to incorporate the missing information into the model and propose two two-stage distributed algorithms, IPW-ADMM and IPW-renewable, for efficient and privacy-preserving estimation of WQR. Both methods first estimate the missingness mechanism using a logistic model and then solve the WQR problem in a communication-efficient manner without sharing raw individual-level data. The IPW-ADMM algorithm parallelizes estimation using a multi-block alternating direction method of multipliers (ADMM), reformulating the nonsmooth WQR objective into a set of local subproblems. The IPW-renewable algorithm adopts a sequential renewable estimation framework with the smoothing technique, making it suitable for streaming or incremental data settings. Simulation studies demonstrate that both proposed methods achieve estimation accuracy comparable to the classical centralized interior point (IP) method, while offering substantial computational speedups in distributed environments. An application to the UK Biobank dataset further illustrates their practical utility in detecting heterogeneous genetic associations across quantiles under covariate missingness.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at https://doi.org/10.1007/s12561-026-09528-6.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"18 3","pages":"844-875"},"PeriodicalIF":0.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13588827/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Canopy2: Tumor Phylogeny Inference by Bulk DNA and Single-Cell RNA Sequencing.","authors":"Ann Marie K Weideman, Rujin Wang, Joseph G Ibrahim, Yuchao Jiang","doi":"10.1007/s12561-024-09466-1","DOIUrl":"10.1007/s12561-024-09466-1","url":null,"abstract":"<p><p>Tumors are comprised of a mixture of distinct cell populations that differ in terms of genetic makeup and function. Such heterogeneity plays a role in the development of drug resistance and the ineffectiveness of targeted cancer therapies. Insight into this complexity can be obtained through the construction of a phylogenetic tree, which illustrates the evolutionary lineage of tumor cells as they acquire mutations over time. We propose Canopy2, a Bayesian framework that uses single nucleotide variants derived from bulk DNA and single-cell RNA sequencing to infer tumor phylogeny and conduct mutational profiling of tumor subpopulations. Canopy2 uses Markov chain Monte Carlo methods to sample from a joint probability distribution involving a mixture of binomial and beta-binomial distributions, specifically chosen to account for the sparsity and stochasticity of the single-cell data. Canopy2 demystifies the sources of zeros in the single-cell data and separates zeros categorized as non-cancerous (cells without mutations), stochastic (mutations not expressed due to bursting), and technical (expressed mutations not picked up by sequencing). Simulations demonstrate that Canopy2 consistently outperforms competing methods and reconstructs the clonal tree with high fidelity, even in situations involving low sequencing depth, poor single-cell yield, and highly-advanced and polyclonal tumors. We further assess the performance of Canopy2 through application to breast cancer and glioblastoma data, benchmarking against existing methods. Canopy2 is an open-source R package available at https://github.com/annweideman/canopy2.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"18 1","pages":"68-110"},"PeriodicalIF":0.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12904911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146203219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multilevel Multivariate Functional Principal Component Analysis of Evoked and Induced Event-Related Spectral Perturbations.","authors":"Mingfei Dong, Donatello Telesca, Abigail Dickinson, Catherine Sugar, Sara J Webb, Shafali Jeste, April R Levin, Frederick Shic, Adam Naples, Susan Faja, Geraldine Dawson, James C McPartland, Damla Şentürk","doi":"10.1007/s12561-025-09510-8","DOIUrl":"10.1007/s12561-025-09510-8","url":null,"abstract":"<p><p>Event-related spectral perturbations (ERSPs) capture dynamic changes in electroencephalography (EEG) power across frequency and trial time. Even though they are obtained at the trial level, they are commonly averaged across trials and analyzed at the subject level for enhancing the signal-to-noise ratio. While evoked activity is stimulus-locked, representing the brain's predictable response to stimuli, induced signals that are not strictly locked to stimulus presentation are thought to be generated by higher-order processes, such as attention and integration. Motivated by joint modeling of multilevel (trials nested in subjects) and multivariate (evoked and induced) ERSP data from a visual-evoked potentials (VEP) task, we propose a multilevel multivariate functional principal components analysis (FPCA) for high-dimensional functional outcomes as a function of time and frequency. The proposed estimation procedure utilizes multilevel univariate FPCA decompositions along each variate of the multivariate outcome using fast covariance estimation and incorporates the dependency across outcome variates at each level of the data. Hence, the proposed approach for multilevel multivariate FPCA can efficiently scale up to higher dimensional functional outcomes and increasing number of variates in the multivariate functional outcome vector. Extensive simulations show the efficacy of the proposed approach, while applications to VEP data lead to new insights on autism-specific neural activity patterns. The autistic group shows significantly lower evoked and higher induced gamma power compared to the neurotypical group. In addition, while subject level variation is dominated by variation in the stimulus-locked evoked signal in neurotypical development, it is dominated by induced power in autism.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12834560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146067612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Deep Learning Prediction of TCR-HLA Associations.","authors":"Fumin Li, Si Liu, Wei Sun","doi":"10.1007/s12561-025-09506-4","DOIUrl":"10.1007/s12561-025-09506-4","url":null,"abstract":"<p><p>Understanding the relationship between T cell receptors (TCRs) and human leukocyte antigens (HLAs) is essential for elucidating immune response specificity, uncovering mechanisms of autoimmunity, and advancing targeted immunotherapies. We have previously developed a deep learning method, DePTH (Deep Learning Prediction of TCR-HLA associations), to predict the association between a TCR and an HLA based on their amino acid sequences. In this work, we evaluated the performance of DePTH in two additional datasets, and investigated the influence of two potential confounding factors: TCR generation probability and the sequence length of CDR3 (Complementarity-Determining Region 3), which is a key region in the antigen-binding site of TCRs. Building on these insights, we combined training data from two datasets to train a new version of DePTH: DePTH 2.0.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13367516/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148449844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weighted Brier Score - an Overall Summary Measure for Risk Prediction Models with Clinical Utility Consideration.","authors":"Kehao Zhu, Yingye Zheng, Kwun Chuen Gary Chan","doi":"10.1007/s12561-025-09505-5","DOIUrl":"10.1007/s12561-025-09505-5","url":null,"abstract":"<p><p>As advancements in novel biomarker-based algorithms and models accelerate their use in disease risk prediction, it is crucial to evaluate these models within the context of their intended clinical application. Prediction models output the absolute risk of disease; subsequently, patient counseling and shared decision-making are based on the estimated individual risk and cost-benefit assessment. The overall impact of the application is referred to as clinical utility, which received significant attention and desire to incorporate into model assessment lately. The classic Brier score is a popular measure of prediction accuracy; however, it is insufficient for effectively assessing clinical utility. To address this limitation, we propose a class of weighted Brier scores that aligns with the decision-theoretic framework of clinical utility. Additionally, we decompose the weighted Brier score into discrimination and calibration components, and we link the weighted Brier score to the <math><mi>H</mi></math> measure, which has been proposed as an alternative to the area under the receiver operating characteristic curve. This theoretical link to the <math><mi>H</mi></math> measure further supports our weighting method and underscores the essential elements of discrimination and calibration in risk prediction evaluation. The practical use of the weighted Brier score as an overall summary is demonstrated using data from a prostate cancer study.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12523994/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variance Estimation for Weighted Average Treatment Effects.","authors":"Huiyue Li, Yi Liu, Yunji Zhou, Jiajun Liu, Dezhao Fu, Roland A Matsouaka","doi":"10.1007/s12561-025-09503-7","DOIUrl":"10.1007/s12561-025-09503-7","url":null,"abstract":"<p><p>Common variance estimation methods for weighted average treatment effects (WATEs) in observational studies include nonparametric bootstrap and model-based, closed-form sandwich variance estimation. However, the computational cost of bootstrap increases with the size of the data at hand. Besides, some replicates may exhibit random violations of the positivity assumption even when the original data do not. Sandwich variance estimation relies on regularity conditions that may be structurally violated. Moreover, the sandwich variance estimation is model-dependent on the propensity score model, the outcome model, or both; thus it does not have a unified closed-form expression. Recent studies have explored the use of wild bootstrap to estimate the variance of the average treatment effect on the treated (ATT). This technique adopts a one-dimensional, nonparametric, and computationally efficient resampling strategy. In this article, we propose a \"post-weighting\" bootstrap approach as an alternative to the conventional bootstrap, which helps avoid random positivity violations in replicates and improves computational efficiency. We also generalize the wild bootstrap algorithm from ATT to the broader class of WATEs by providing new justification for correctly accounting for sampling variability from multiple sources under different weighting functions. We evaluate the performance of all four methods through extensive simulation studies and demonstrate their application using data from the National Health and Nutrition Examination Survey (NHANES). Our findings offer several practical recommendations for the variance estimation of WATE estimators.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13220987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148139259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}