Statistics in Biosciences最新文献_第2页

Efficiency loss with binary pre-processing of continuous monitoring data. 连续监测数据二进制预处理的效率损失。

IF 0.8

Statistics in Biosciences Pub Date : 2025-01-14 DOI: 10.1007/s12561-025-09473-w

Paula R Langner, Elizabeth Juarez-Colunga, Lucas N Marzec, Gary K Grunwald, John D Rice

{"title":"Efficiency loss with binary pre-processing of continuous monitoring data.","authors":"Paula R Langner, Elizabeth Juarez-Colunga, Lucas N Marzec, Gary K Grunwald, John D Rice","doi":"10.1007/s12561-025-09473-w","DOIUrl":"10.1007/s12561-025-09473-w","url":null,"abstract":"In studies with a recurrent event outcome, events may be captured as counts during subsequent intervals or follow-up times either by design or for ease of analysis. In many cases, recurrent events may be further coarsened such that only an indicator of one or more events in an interval is observed at the follow-up time, resulting in a loss of information relative to a record of all events. In this paper, we examine efficiency loss when coarsening longitudinally observed counts to binary indicators and aspects of the design which impact the ability to estimate a treatment effect of interest. The investigation was motivated by a study of patients with cardiac implantable electronic devices in which investigators aimed to examine the effect of a treatment on events detected by the devices over time. In order to study components of such a recurrent event process impacted by data coarsening, we derive the asymptotic relative efficiency (ARE) of a treatment effect estimator utilizing a coarsened binary outcome relative to an alternative estimator using the count outcome. We compare the efficiencies and consider conditions where the binary process maintains good efficiency in estimating a treatment effect. We present an application of the methods to a data set consisting of seizure counts in a sample of patients with epilepsy.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12266715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660627","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}

引用次数: 0

Bayesian Estimation of Propensity Scores for Integrating Multiple Cohorts with High-Dimensional Covariates. 整合具有高维协变量的多队列的贝叶斯倾向得分估计。

IF 0.4

Statistics in Biosciences Pub Date : 2024-12-09 DOI: 10.1007/s12561-024-09470-5

Subharup Guha, Yi Li

{"title":"Bayesian Estimation of Propensity Scores for Integrating Multiple Cohorts with High-Dimensional Covariates.","authors":"Subharup Guha, Yi Li","doi":"10.1007/s12561-024-09470-5","DOIUrl":"10.1007/s12561-024-09470-5","url":null,"abstract":"Comparative meta-analyses of groups of subjects by integrating multiple observational studies rely on estimated propensity scores (PSs) to mitigate covariate imbalances. However, PS estimation grapples with the theoretical and practical challenges posed by high-dimensional covariates. Motivated by an integrative analysis of breast cancer patients across seven medical centers, this paper tackles the challenges of integrating multiple observational datasets. The proposed inferential technique, called Bayesian Motif Submatrices for Covariates (B-MSC), addresses the curse of dimensionality by a hybrid of Bayesian and frequentist approaches. B-MSC uses nonparametric Bayesian \"Chinese restaurant\" processes to eliminate redundancy in the high-dimensional covariates and discover latent motifs or lower-dimensional structures. With these motifs as potential predictors, standard regression techniques can be utilized to accurately infer the PSs and facilitate covariate-balanced group comparisons. Simulations and meta-analysis of the motivating cancer investigation demonstrate the efficacy of the B-MSC approach to accurately estimate the propensity scores and efficiently address covariate imbalance when integrating observational health studies with high-dimensional covariates.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12320959/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144973414","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}

引用次数: 0

Model Checking for Logistic Models with Study of Telehealth During the COVID-19 Pandemic Among PWH in DC. 新型冠状病毒病疫情期间DC PWH远程医疗Logistic模型检验

IF 0.4

Statistics in Biosciences Pub Date : 2024-09-27 DOI: 10.1007/s12561-024-09457-2

Zeyu Yang, Hua Liang, Huiling Liu, Shannon Barth, Morgan Byrne, Elisabeth Andersen, Vinay Bhandaru, Amanda Castel

引用次数: 0

Enhancing Genetic Risk Prediction through Federated Semi-Supervised Transfer Learning with Inaccurate Electronic Health Record Data. 基于不准确电子病历数据的联邦半监督迁移学习增强遗传风险预测。

IF 0.4

Statistics in Biosciences Pub Date : 2024-08-13 DOI: 10.1007/s12561-024-09449-2

Yuying Lu, Tian Gu, Rui Duan

{"title":"Enhancing Genetic Risk Prediction through Federated Semi-Supervised Transfer Learning with Inaccurate Electronic Health Record Data.","authors":"Yuying Lu, Tian Gu, Rui Duan","doi":"10.1007/s12561-024-09449-2","DOIUrl":"10.1007/s12561-024-09449-2","url":null,"abstract":"Large-scale genomics data combined with Electronic Health Records (EHRs) illuminate the path towards personalized disease management and enhanced medical interventions. However, the absence of \"gold standard\" disease labels makes the development of machine learning models a challenging task. Additionally, imbalances in demographic representation within datasets compromise the development of unbiased healthcare solutions. In response to these challenges, we introduce FEderated Semi-Supervised Transfer Learning (FEST) for improving disease risk predictions in underrepresented populations. FEST facilitates the collaborative training of models across various institutions by leveraging both labeled and unlabeled data from diverse subpopulations. It addresses distributional variations across different populations and healthcare institutions by combining density ratio reweighting and model calibration techniques. Federated learning algorithms are developed for training models using only summary-level statistics. We perform simulation studies to assess the efficacy of FEST in comparisons with a few alternative methods. Subsequently, we apply FEST to training a genetic risk prediction model for type 2 diabetes that targets the African-Ancestry population using data from the Massachusetts General Brigham (MGB) Biobank. Both our computational experiments and real-world data application underline the superior performance of FEST over competing methods.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12409711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145013364","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}

引用次数: 0

Graph-guided Bayesian Factor Model for Integrative Analysis of Multi-modal Data with Noisy Network Information. 带噪声网络信息的多模态数据综合分析的图导贝叶斯因子模型。

IF 0.4

Statistics in Biosciences Pub Date : 2024-08-11 DOI: 10.1007/s12561-024-09452-7

Wenrui Li, Qiyiwen Zhang, Kewen Qu, Qi Long

{"title":"Graph-guided Bayesian Factor Model for Integrative Analysis of Multi-modal Data with Noisy Network Information.","authors":"Wenrui Li, Qiyiwen Zhang, Kewen Qu, Qi Long","doi":"10.1007/s12561-024-09452-7","DOIUrl":"10.1007/s12561-024-09452-7","url":null,"abstract":"There is a growing body of literature on factor analysis that can capture individual and shared structures in multi-modal data. However, few of these approaches incorporate biological knowledge such as functional genomics and functional metabolomics. Graph-guided statistical learning methods that can incorporate knowledge of underlying networks have been shown to improve predication and classification accuracy, and yield more interpretable results. Moreover, these methods typically use graphs extracted from existing databases or rely on subject matter expertise which are known to be incomplete and may contain false edges. To address this gap, we propose a graph-guided Bayesian factor model that can account for network noise and identify globally shared, partially shared and modality-specific latent factors in multimodal data. Specifically, we use two sources of network information, including the noisy graph extracted from existing databases and the estimated graph from observed features in the dataset at hand, to inform the model for the true underlying network via a latent scale modeling framework. This model is coupled with the Bayesian factor analysis model with shrinkage priors to encourage feature-wise and modal-wise sparsity, thereby allowing feature selection and identification of factors of each type. We develop an efficient Markov chain Monte Carlo algorithm for posterior sampling. We demonstrate the advantages of our method over existing methods in simulations, and through analyses of gene expression and metabolomics datasets for Alzheimer's disease.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12221265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144691908","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}

引用次数: 0

Joint Modeling of Geometric Features of Longitudinal Process and Discrete Survival Time Measured on Nested Timescales: An Application to Fecundity Studies. 纵向过程几何特征和嵌套时间尺度上的离散生存时间联合建模:在繁殖力研究中的应用

IF 0.4

Statistics in Biosciences Pub Date : 2024-04-01 Epub Date: 2023-08-11 DOI: 10.1007/s12561-023-09381-x

Abhisek Saha, Ling Ma, Animikh Biswas, Rajeshwari Sundaram

{"title":"Joint Modeling of Geometric Features of Longitudinal Process and Discrete Survival Time Measured on Nested Timescales: An Application to Fecundity Studies.","authors":"Abhisek Saha, Ling Ma, Animikh Biswas, Rajeshwari Sundaram","doi":"10.1007/s12561-023-09381-x","DOIUrl":"10.1007/s12561-023-09381-x","url":null,"abstract":"In biomedical studies, longitudinal processes are collected till time-to-event, sometimes on nested timescales (example, days within months). Most of the literature in joint modeling of longitudinal and time-to-event data has focused on modeling the mean or dispersion of the longitudinal process with the hazard for time-to-event. However, based on the motivating studies, it may be of interest to investigate how the cycle-level geometric features (such as the curvature, location and height of a peak), of a cyclical longitudinal process is associated with the time-to-event being studied. We propose a shared parameter joint model for a cyclical longitudinal process and a discrete survival time, measured on nested timescales, where the cycle-varying geometric feature is modeled through a linear mixed effects model and a proportional hazards model for the discrete survival time. The proposed approach allows for prediction of survival probabilities for future subjects based on their available longitudinal measurements. Our proposed model and approach is illustrated through simulation and analysis of Stress and Time-to-Pregnancy, a component of Oxford Conception Study. A joint modeling approach was used to assess whether the cycle-specific geometric features of the lutenizing hormone measurements, such as its peak or its curvature, are associated with time-to-pregnancy (TTP).","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"1 1","pages":"86-106"},"PeriodicalIF":0.4,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12687766/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46399657","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}

引用次数: 0

Use of Real-World EMR Data to Rapidly Evaluate Treatment Effects of Existing Drugs for Emerging Infectious Diseases: Remdesivir for COVID-19 Treatment as an Example 利用真实世界的 EMR 数据快速评估现有药物对新发传染病的治疗效果：以治疗 COVID-19 的雷米地韦为例

IF 1

Statistics in Biosciences Pub Date : 2024-01-02 DOI: 10.1007/s12561-023-09411-8

Chenguang Zhang, Masayuki Nigo, Shivani Patel, Duo Yu, Edward Septimus, Hulin Wu

引用次数: 0

Modeling Historic Arsenic Exposures and Spatial Risk for Bladder Cancer 砷历史暴露与膀胱癌空间风险建模

IF 1

Statistics in Biosciences Pub Date : 2023-12-17 DOI: 10.1007/s12561-023-09404-7

Joseph Boyle, Mary H. Ward, Stella Koutros, M. Karagas, M. Schwenn, Alison T. Johnson, Debra T. Silverman, David C. Wheeler

引用次数: 0

Considerations and Targeted Approaches to Identifying Bad Actors in Exposure Mixtures 识别暴露混合物中不良作用因子的考虑因素和有针对性的方法

IF 1

Statistics in Biosciences Pub Date : 2023-12-12 DOI: 10.1007/s12561-023-09409-2

Alexander P. Keil, Katie M. O’Brien

引用次数: 0

Causal Mediation Tree Model for Feature Identification on High-Dimensional Mediators 用于识别高维中介特征的因果中介树模型

IF 1

Statistics in Biosciences Pub Date : 2023-12-02 DOI: 10.1007/s12561-023-09402-9

Yao Li, Wei Xu

引用次数: 0