Statistics in Biosciences最新文献_第3页

Mediation Analysis with Random Distribution as Mediator with an Application to iCOMPARE Trial 随机分布作为中介的中介分析及其在iccompare试验中的应用

IF 1

Statistics in Biosciences Pub Date : 2023-08-30 DOI: 10.1007/s12561-023-09383-9

Jingru Zhang, M. Basner, Christopher W Jones, D. Dinges, H. Shou, Hongzhe Li

引用次数: 0

Simultaneous Denoising and Heterogeneity Learning for Time Series Data 时间序列数据的同时去噪和异质性学习

IF 1

Statistics in Biosciences Pub Date : 2023-08-24 DOI: 10.1007/s12561-023-09384-8

Xiwen Jiang, Weining Shen

引用次数: 0

Understanding Effective Virus Control Policies for Covid-19 with the Q-learning Method 用q -学习方法理解Covid-19有效的病毒控制策略

IF 1

Statistics in Biosciences Pub Date : 2023-08-11 DOI: 10.1007/s12561-023-09382-w

Yasin Khadem Charvadeh, G. Yi

引用次数: 0

Semiparametric Trend Analysis for Stratified Recurrent Gap Times Under Weak Comparability Constraint. 弱可比性约束下分层循环间隙时间的半参数趋势分析

IF 0.8

Statistics in Biosciences Pub Date : 2023-07-01 Epub Date: 2023-06-03 DOI: 10.1007/s12561-023-09376-8

Peng Liu, Yijian Huang, Kwun Chuen Gary Chan, Ying Qing Chen

{"title":"Semiparametric Trend Analysis for Stratified Recurrent Gap Times Under Weak Comparability Constraint.","authors":"Peng Liu, Yijian Huang, Kwun Chuen Gary Chan, Ying Qing Chen","doi":"10.1007/s12561-023-09376-8","DOIUrl":"10.1007/s12561-023-09376-8","url":null,"abstract":"Recurrent event data are frequently encountered in many longitudinal studies where each individual may experience more than one event. Wang and Chen (Biometrics 56(3):789-794, 2000) proposed a comparability constraint to estimate the time trend for the gap times, where the gap time pairs that satisfy the constraint have the same conditional distribution. However, the comparable paired gap times are also independent. Therefore, the comparable gap time pairs will be subject to a stronger constraint than needed for the estimation. Thus their procedure is subject to information loss. Under the accelerated failure time model, we propose a new comparability constraint that can overcome the drawback mentioned above. The gap time pairs being selected by the proposed comparability constraint will still have the same distribution, but they do not need to be independent of each other. We showed that the proposed comparability constraint will utilize more gap time data pairs than the strong comparability. And we showed via various simulation studies that the variance will be smaller than Wang and Chen's (2000) estimator. We apply the proposed method to the HIV Prevention Trial Network 052 study.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"15 1","pages":"455-474"},"PeriodicalIF":0.8,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11542620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45506409","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

Statistical Learning of Large-Scale Genetic Data: How to Run a Genome-Wide Association Study of Gene-Expression Data Using the 1000 Genomes Project Data 大规模遗传数据的统计学习:如何使用1000基因组计划数据进行基因表达数据的全基因组关联研究

Statistics in Biosciences Pub Date : 2023-07-01 DOI: 10.1007/s12561-023-09375-9

Anton Sugolov, Eric Emmenegger, Andrew D. Paterson, Lei Sun

{"title":"Statistical Learning of Large-Scale Genetic Data: How to Run a Genome-Wide Association Study of Gene-Expression Data Using the 1000 Genomes Project Data","authors":"Anton Sugolov, Eric Emmenegger, Andrew D. Paterson, Lei Sun","doi":"10.1007/s12561-023-09375-9","DOIUrl":"https://doi.org/10.1007/s12561-023-09375-9","url":null,"abstract":"Abstract Teaching statistics through engaging applications to contemporary large-scale datasets is essential to attracting students to the field. To this end, we developed a hands-on, week-long workshop for senior high-school or junior undergraduate students, without prior knowledge in statistical genetics but with some basic knowledge in data science, to conduct their own genome-wide association study (GWAS). The GWAS was performed for open source gene expression data, using publicly available human genetics data. Assisted by a detailed instruction manual, students were able to obtain $$sim$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mo>∼</mml:mo> </mml:math> 1.4 million p-values from a real scientific study, within several days. This early motivation kept students engaged in learning the theories that support their results, including regression, data visualization, results interpretation, and large-scale multiple hypothesis testing. To further their learning motivation by emphasizing the personal connection to this type of data analysis, students were encouraged to make short presentations about how GWAS has provided insights into the genetic basis of diseases that are present in their friends or families. The appended open source, step-by-step instruction manual includes descriptions of the datasets used, the software needed, and results from the workshop. Additionally, scripts used in the workshop are archived on Github and Zenodo to further enhance reproducible research and training.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135315171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating Dynamic Discrimination Performance of Risk Prediction Models for Survival Outcomes. 评估生存结果风险预测模型的动态判别性能。

IF 0.8

Statistics in Biosciences Pub Date : 2023-07-01 Epub Date: 2023-02-02 DOI: 10.1007/s12561-023-09362-0

Jing Zhang, Jing Ning, Ruosha Li

{"title":"Evaluating Dynamic Discrimination Performance of Risk Prediction Models for Survival Outcomes.","authors":"Jing Zhang, Jing Ning, Ruosha Li","doi":"10.1007/s12561-023-09362-0","DOIUrl":"10.1007/s12561-023-09362-0","url":null,"abstract":"Risk prediction models for survival outcomes are widely applied in medical research to predict future risk for the occurrence of the event. In many clinical studies, the biomarker data are measured repeatedly over time. To facilitate timely disease prognosis and decision making, many dynamic prediction models have been developed and generate predictions on a real-time basis. As a dynamic prediction model updates an individual's risk prediction over time based on new measurements, it is often important to examine how well the model performs at different measurement times and prediction times. In this article, we propose a two-dimensional area under curve (AUC) measure for dynamic prediction models and develop associated estimation and inference procedures. The estimation procedures are discussed under two types of biomarker measurement schedules: regular visits and irregular visits. The model parameters are estimated effectively by maximizing a pseudo-partial likelihood function. We apply the proposed method to a renal transplantation study to evaluate the discrimination performance of dynamic prediction models based on longitudinal biomarkers for graft failure.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"15 2","pages":"353-371"},"PeriodicalIF":0.8,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10483238/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10588040","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

Longitudinal Associations Between Timing of Physical Activity Accumulation and Health: Application of Functional Data Methods. 体育锻炼时间与健康之间的纵向联系：功能数据方法的应用

IF 0.8

Statistics in Biosciences Pub Date : 2023-07-01 Epub Date: 2022-09-29 DOI: 10.1007/s12561-022-09359-1

Wenyi Lin, Jingjing Zou, Chongzhi Di, Dorothy D Sears, Cheryl L Rock, Loki Natarajan

{"title":"Longitudinal Associations Between Timing of Physical Activity Accumulation and Health: Application of Functional Data Methods.","authors":"Wenyi Lin, Jingjing Zou, Chongzhi Di, Dorothy D Sears, Cheryl L Rock, Loki Natarajan","doi":"10.1007/s12561-022-09359-1","DOIUrl":"10.1007/s12561-022-09359-1","url":null,"abstract":"Accelerometers are widely used for tracking human movement and provide minute-level (or even 30 Hz level) physical activity (PA) records for detailed analysis. Instead of using day-level summary statistics to assess these densely sampled inputs, we implement functional principal component analysis (FPCA) approaches to study the temporal patterns of PA data from 245 overweight/obese women at three visits over a 1-year period. We apply longitudinal FPCA to decompose PA inputs, incorporating subject-specific variability, and then test the association between these patterns and obesity-related health outcomes by multiple mixed effect regression models. With the proposed methods, the longitudinal patterns in both densely sampled inputs and scalar outcomes are investigated and connected. The results show that the health outcomes are strongly associated with PA variation, in both subject and visit-level. In addition, we reveal that timing of PA during the day can impact changes in outcomes, a finding that would not be possible with day-level PA summaries. Thus, our findings imply that the use of longitudinal FPCA can elucidate temporal patterns of multiple levels of PA inputs. Furthermore, the exploration of the relationship between PA patterns and health outcomes can be useful for establishing weight-loss guidelines.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"15 2","pages":"309-329"},"PeriodicalIF":0.8,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9799319","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

Classification of Free-Living Body Posture with ECG Patch Accelerometers: Application to the Multicenter AIDS Cohort Study 心电图贴片加速度计对自由生活体位的分类:在多中心艾滋病队列研究中的应用

Statistics in Biosciences Pub Date : 2023-06-28 DOI: 10.1007/s12561-023-09377-7

Lacey H. Etzkorn, Amir S. Heravi, Nicolas D. Knuth, Katherine C. Wu, Wendy S. Post, Jacek K. Urbanek, Ciprian M. Crainiceanu

{"title":"Classification of Free-Living Body Posture with ECG Patch Accelerometers: Application to the Multicenter AIDS Cohort Study","authors":"Lacey H. Etzkorn, Amir S. Heravi, Nicolas D. Knuth, Katherine C. Wu, Wendy S. Post, Jacek K. Urbanek, Ciprian M. Crainiceanu","doi":"10.1007/s12561-023-09377-7","DOIUrl":"https://doi.org/10.1007/s12561-023-09377-7","url":null,"abstract":"As health studies increasingly monitor free-living heart performance via ECG patches with accelerometers, researchers will seek to investigate cardio-electrical responses to physical activity and sedentary behavior, increasing demand for fast, scalable methods to process accelerometer data. We extend a posture classification algorithm for accelerometers in ECG patches when researchers do not have ground-truth labels or other reference measurements (i.e., upright measurement). Men living with and without HIV in the Multicenter AIDS Cohort study wore the Zio XT® for up to 2 weeks (n = 1250). Our novel extensions for posture classification include (1) estimation of an upright posture for each individual without a reference upright measurement; (2) correction of the upright estimate for device removal and re-positioning using novel spherical change point detection; and (3) classification of upright and recumbent periods using a clustering and voting process rather than a simple inclination threshold used in other algorithms. As no posture labels exist in the free-living environment, we perform numerous sensitivity analyses and evaluate the algorithm against labeled data from the Towson Accelerometer Study, where participants wore accelerometers at the waist. On average, 87.1% of participants were recumbent at 4 a.m. and 15.5% were recumbent at 1 p.m. Participants were recumbent 54 min longer on weekends compared to weekdays. Performance was good in comparison to labeled data in a separate, controlled setting (accuracy = 96.0%, sensitivity = 97.5%, specificity = 95.9%). Posture may be classified in the free-living environment from accelerometers in ECG patches even without measuring a standard upright position. Furthermore, algorithms that fail to account for individuals who rotate and re-attach the accelerometer may fail in the free-living environment.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135260639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Doubly Robust Semiparametric Estimation for Multi-group Causal Comparisons 多组因果比较的双稳健半参数估计

IF 1

Statistics in Biosciences Pub Date : 2023-06-24 DOI: 10.1007/s12561-023-09378-6

Anqi Yin, Ao Yuan, M. Tan

引用次数: 0

Retraction Note: Positive Stable Shared Frailty Models Based on Additive Hazards 注:基于加性危险的正稳定共享脆弱性模型

Statistics in Biosciences Pub Date : 2023-06-15 DOI: 10.1007/s12561-023-09380-y

David D. Hanagal

引用次数: 0