Pub Date : 2023-07-01Epub Date: 2023-02-02DOI: 10.1007/s12561-023-09362-0
Jing Zhang, Jing Ning, Ruosha Li
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.
{"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":"<p><p>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.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"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}
Pub Date : 2023-07-01Epub Date: 2022-09-29DOI: 10.1007/s12561-022-09359-1
Wenyi Lin, Jingjing Zou, Chongzhi Di, Dorothy D Sears, Cheryl L Rock, Loki Natarajan
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.
加速度计被广泛用于追踪人体运动，并提供分钟级（甚至 30 Hz 级）的体力活动（PA）记录以供详细分析。我们采用功能主成分分析 (FPCA) 方法来研究 245 名超重/肥胖女性在一年内三次访问中的体力活动数据的时间模式，而不是使用日级汇总统计来评估这些密集采样的输入数据。我们采用纵向功能主成分分析法对 PA 输入进行分解，将特定受试者的变异性纳入其中，然后通过多重混合效应回归模型检验这些模式与肥胖相关健康结果之间的关联。利用所提出的方法，对密集采样输入和标量结果的纵向模式进行了研究和连接。结果表明，在受试者和访问水平上，健康结果与 PA 变化密切相关。此外，我们还揭示了一天中锻炼的时间会对结果的变化产生影响，而这一发现在一天的锻炼总结中是不可能出现的。因此，我们的研究结果表明，使用纵向 FPCA 可以阐明多层次 PA 输入的时间模式。此外，探索活动量模式与健康结果之间的关系有助于制定减肥指南。
{"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":"<p><p>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.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"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}
Pub Date : 2023-06-28DOI: 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
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-06-24DOI: 10.1007/s12561-023-09378-6
Anqi Yin, Ao Yuan, M. Tan
{"title":"Doubly Robust Semiparametric Estimation for Multi-group Causal Comparisons","authors":"Anqi Yin, Ao Yuan, M. Tan","doi":"10.1007/s12561-023-09378-6","DOIUrl":"https://doi.org/10.1007/s12561-023-09378-6","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"52603356","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}
Pub Date : 2023-06-15DOI: 10.1007/s12561-023-09380-y
David D. Hanagal
{"title":"Retraction Note: Positive Stable Shared Frailty Models Based on Additive Hazards","authors":"David D. Hanagal","doi":"10.1007/s12561-023-09380-y","DOIUrl":"https://doi.org/10.1007/s12561-023-09380-y","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134890760","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}
Pub Date : 2023-06-03DOI: 10.1007/s12561-023-09373-x
Chi-Kuang Yeh, Peijun Sang
{"title":"Variable Selection in Multivariate Functional Linear Regression","authors":"Chi-Kuang Yeh, Peijun Sang","doi":"10.1007/s12561-023-09373-x","DOIUrl":"https://doi.org/10.1007/s12561-023-09373-x","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43640560","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}
Pub Date : 2023-04-11DOI: 10.1007/s12561-023-09371-z
Xuan Ye, Heng Li
{"title":"A Non-parametric Test Based on Local Pairwise Comparisons of Patients for Single and Composite Endpoints","authors":"Xuan Ye, Heng Li","doi":"10.1007/s12561-023-09371-z","DOIUrl":"https://doi.org/10.1007/s12561-023-09371-z","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46016367","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}
Pub Date : 2023-04-08DOI: 10.1007/s12561-023-09365-x
Jingjing Ye, Hong Tian, Xiang Guo, Naitee Ting
{"title":"Clinical Trial Design—What is the Critical Question for Decision-Making?","authors":"Jingjing Ye, Hong Tian, Xiang Guo, Naitee Ting","doi":"10.1007/s12561-023-09365-x","DOIUrl":"https://doi.org/10.1007/s12561-023-09365-x","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48322598","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}
Pub Date : 2023-04-07DOI: 10.1007/s12561-023-09367-9
Qingyang Liu, Yuping Zhang
{"title":"Integrative Structural Learning of Mixed Graphical Models via Pseudo-likelihood","authors":"Qingyang Liu, Yuping Zhang","doi":"10.1007/s12561-023-09367-9","DOIUrl":"https://doi.org/10.1007/s12561-023-09367-9","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45996197","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}
Pub Date : 2023-04-02DOI: 10.1007/s12561-023-09369-7
B. Ryan, Ananthika Nirmalkanna, Candemir Çigsar, Yildiz E. Yilmaz
{"title":"Evaluation of Designs and Estimation Methods Under Response-Dependent Two-Phase Sampling for Genetic Association Studies","authors":"B. Ryan, Ananthika Nirmalkanna, Candemir Çigsar, Yildiz E. Yilmaz","doi":"10.1007/s12561-023-09369-7","DOIUrl":"https://doi.org/10.1007/s12561-023-09369-7","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43235691","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}
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