Statistics in Biosciences最新文献_第6页

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

Variable Selection in Multivariate Functional Linear Regression 多元函数线性回归中的变量选择

IF 1

Statistics in Biosciences Pub Date : 2023-06-03 DOI: 10.1007/s12561-023-09373-x

Chi-Kuang Yeh, Peijun Sang

引用次数: 1

A Non-parametric Test Based on Local Pairwise Comparisons of Patients for Single and Composite Endpoints 基于单终点和复合终点患者局部两两比较的非参数检验

IF 1

Statistics in Biosciences Pub Date : 2023-04-11 DOI: 10.1007/s12561-023-09371-z

Xuan Ye, Heng Li

引用次数: 0

Clinical Trial Design—What is the Critical Question for Decision-Making? 临床试验设计——决策的关键问题是什么？

IF 1

Statistics in Biosciences Pub Date : 2023-04-08 DOI: 10.1007/s12561-023-09365-x

Jingjing Ye, Hong Tian, Xiang Guo, Naitee Ting

引用次数: 0

Integrative Structural Learning of Mixed Graphical Models via Pseudo-likelihood 基于伪似然的混合图形模型综合结构学习

IF 1

Statistics in Biosciences Pub Date : 2023-04-07 DOI: 10.1007/s12561-023-09367-9

Qingyang Liu, Yuping Zhang

引用次数: 0

Evaluation of Designs and Estimation Methods Under Response-Dependent Two-Phase Sampling for Genetic Association Studies 遗传关联研究中响应相关两阶段抽样的设计与估计方法评价

IF 1

Statistics in Biosciences Pub Date : 2023-04-02 DOI: 10.1007/s12561-023-09369-7

B. Ryan, Ananthika Nirmalkanna, Candemir Çigsar, Yildiz E. Yilmaz

引用次数: 0

On High-Dimensional Covariate Adjustment for Estimating Causal Effects in Randomized Trials with Survival Outcomes. 关于在有生存结果的随机试验中估算因果效应的高维变量调整。

IF 1

Statistics in Biosciences Pub Date : 2023-04-01 Epub Date: 2022-09-25 DOI: 10.1007/s12561-022-09358-2

Ran Dai, Cheng Zheng, Mei-Jie Zhang

{"title":"On High-Dimensional Covariate Adjustment for Estimating Causal Effects in Randomized Trials with Survival Outcomes.","authors":"Ran Dai, Cheng Zheng, Mei-Jie Zhang","doi":"10.1007/s12561-022-09358-2","DOIUrl":"10.1007/s12561-022-09358-2","url":null,"abstract":"The purpose of this work is to improve the efficiency in estimating the average causal effect (ACE) on the survival scale where right-censoring exists and high-dimensional covariate information is available. We propose new estimators using regularized survival regression and survival Random Forest (RF) to adjust for the high-dimensional covariate to improve efficiency. We study the behavior of the adjusted estimators under mild assumptions and show theoretical guarantees that the proposed estimators are more efficient than the unadjusted ones asymptotically when using RF for the adjustment. In addition, these adjusted estimators are <math> <mrow><msqrt><mi>n</mi></msqrt> </mrow> </math> - consistent and asymptotically normally distributed. The finite sample behavior of our methods is studied by simulation. The simulation results are in agreement with the theoretical results. We also illustrate our methods by analyzing the real data from transplant research to identify the relative effectiveness of identical sibling donors compared to unrelated donors with the adjustment of cytogenetic abnormalities.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"15 1","pages":"242-260"},"PeriodicalIF":1.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9777456","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