Statistics in Biosciences生物科学中的统计学
Statistics in Biosciences(英文缩写 STAT BIOSCI),ISSN 1867-1764,eISSN 1867-1772,中文译名:生物科学中的统计学 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 未收录 | N/A |
| 2022 | 1.000 | N/A |
| 2023 | 0.800 | Q4 |
| 2024 | 0.400 | Q4 |
| 2025 | 0.500 | Q4 |
Statistics in Biosciences 最新收录文献
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1. Distributed quantile regression for big data with missing covariates.
PMID:日期:2026-07-09Data 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. The online version contains supplementary material available at https://doi.org/10.1007/s12561-026-09528-6.
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2. Contrastive Latent Functional Model.
PMID:日期:2026-05-23Functional 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.
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3. ReFIT: Federated Transfer Learning for Sequential Prediction and Uncertainty Quantification Using Streaming EHR Data.
PMID:日期:2026-05-23Modern 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.
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4. Multilevel Multivariate Functional Principal Component Analysis of Evoked and Induced Event-Related Spectral Perturbations.
PMID:日期:2025-12-29Event-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.
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5. Improved Deep Learning Prediction of TCR-HLA Associations.
PMID:日期:2025-10-06Understanding 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.
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6. Weighted Brier Score - an Overall Summary Measure for Risk Prediction Models with Clinical Utility Consideration.
PMID:日期:2025-09-18As 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 measure, which has been proposed as an alternative to the area under the receiver operating characteristic curve. This theoretical link to the 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.
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7. Variance Estimation for Weighted Average Treatment Effects.
PMID:日期:2025-09-15Common 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.
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8. Robust Privacy-Preserving Models for Cluster-Level Confounding: Recognizing Disparities in Access to Transplantation.
PMID:日期:2025-07-07In health services applications where the patients are clustered within common institutions or geographic regions, it is often of interest to estimate the treatment effects of the medical providers after adjusting for confounding risk factors that are related to patients' choices of provider but beyond the providers' control. While most existing risk-adjustment methods are only capable of controlling for patient-level confounding risk factors (e.g., age or comorbidities), there are often important cluster-level confounding variables (e.g., regional or community-level risk factors) that should be accounted for in provider evaluations. These adjustments for cluster-level confounding factors are further complicated by the limited availability of protected patient health data, the inevitable influence of unobservable confounding factors, and the presence of outlying cluster units. To address these issues, we propose a privacy-preserving model and a novel Pseudo-Bayesian inference method to robustly assess the providers' treatment effects with adjustments for observed cluster-level confounders and corrections for overdispersion from unobserved cluster-level confounding factors. We derive theoretical connections between our proposed estimation method and the Correlated Random Effects model, uncovering several advantages in terms of estimation stability, computational efficiency, and privacy preservation. Motivated by efforts to improve equity in transplant care, we apply these methods to evaluate transplant centers while adjusting for observed geographic disparities in donor organ availability and correcting for overdispersion from unobservable confounding factors, such as the complex impact of the COVID-19 pandemic.
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9. Accounting for Competing Risks in the Assessment of Prognostic Biomarkers' Discriminative Accuracy.
PMID:日期:2025-07-07The discriminative performance of biomarkers often changes over time and exhibits heterogeneity across subgroups defined by patient characteristics. Assessing how this performance varies with these factors is crucial for a comprehensive evaluation of biomarkers and to identify areas for improvement in sub-populations with poor performance. Additionally, the presence of competing risks complicates the assessment of discriminative performance. Ignoring competing risks can lead to misleading conclusions, as the biomarker's performance for the event of interest, such as disease onset, may be confounded by its performance for competing events, such as death. To address these challenges, we develop a regression model to assess the impact of covariates on the discriminative performance of biomarkers, characterized by the covariate-specific time-dependent Area-undercurve (AUC) for a specific cause. We construct a pseudo partial-likelihood for estimation and inference and establish the asymptotic properties of the proposed estimators. Through simulation studies, we demonstrate the finite sample performance of these estimators, and we apply the proposed method to data from the African American Study of Kidney Disease and Hypertension (AASK).
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10. Central Posterior Envelopes for Bayesian Longitudinal Functional Principal Component Analysis.
PMID:日期:2025-07-05Longitudinally observed functional data are commonly encountered in biomedical studies. Under the weak separability assumption of the high dimensional covariance, the recently proposed Bayesian longitudinal functional principal component analysis (B-LFPCA) achieves the decomposition of the multidimensional signal into highly interpretable lower dimensional summaries, including eigenfunctions that capture directions of variation in the data along the longitudinal and functional dimensions. B-LFPCA provides uncertainty quantification of the estimated functional decomposition components through simultaneous parametric credible bands formed using the posterior sample. However, these traditional summaries are inherently based on point-wise summaries of the estimated functional components and do not take into account the functional nature of the estimated quantities. We introduce central posterior envelopes (CPEs) for uncertainty quantification of the low-dimensional B-LFPCA decomposition components based on functional depth ordering of the posterior estimates. The proposed CPEs are fully data-driven visualization tools, displaying the most-central regions of the posterior sample at specified -level percentile contours. Modified band depth and modified volume depth are utilized to order posterior sample of functional decomposition components, including the mean function and the marginal longitudinal and functional eigenfunctions. The proposed CPEs are applied to analyze the longitudinally observed Event Related Potentials (ERPs) recorded during an implicit learning paradigm, leading to novel insights on longitudinal learning trends across a group of autistic kids and their neurotypical peers. Finally, effectiveness of the proposed CPEs is demonstrated through extensive simulations that explore different scenarios of increased variability in the longitudinal functional data.