Statistics in Biosciences最新文献_第4页

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

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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

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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？

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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

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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

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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。

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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

A functional model for studying common trends across trial time in eye tracking experiments. 用于研究眼动跟踪实验中跨试验时间的共同趋势的功能模型。

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Statistics in Biosciences Pub Date : 2023-04-01 Epub Date: 2022-09-05 DOI: 10.1007/s12561-022-09354-6

Mingfei Dong, Donatello Telesca, Catherine Sugar, Frederick Shic, Adam Naples, Scott P Johnson, Beibin Li, Adham Atyabi, Minhang Xie, Sara J Webb, Shafali Jeste, Susan Faja, April R Levin, Geraldine Dawson, James C McPartland, Damla Şentürk

{"title":"A functional model for studying common trends across trial time in eye tracking experiments.","authors":"Mingfei Dong, Donatello Telesca, Catherine Sugar, Frederick Shic, Adam Naples, Scott P Johnson, Beibin Li, Adham Atyabi, Minhang Xie, Sara J Webb, Shafali Jeste, Susan Faja, April R Levin, Geraldine Dawson, James C McPartland, Damla Şentürk","doi":"10.1007/s12561-022-09354-6","DOIUrl":"10.1007/s12561-022-09354-6","url":null,"abstract":"Eye tracking (ET) experiments commonly record the continuous trajectory of a subject's gaze on a two-dimensional screen throughout repeated presentations of stimuli (referred to as trials). Even though the continuous path of gaze is recorded during each trial, commonly derived outcomes for analysis collapse the data into simple summaries, such as looking times in regions of interest, latency to looking at stimuli, number of stimuli viewed, number of fixations or fixation length. In order to retain information in trial time, we utilize functional data analysis (FDA) for the first time in literature in the analysis of ET data. More specifically, novel functional outcomes for ET data, referred to as viewing profiles, are introduced that capture the common gazing trends across trial time which are lost in traditional data summaries. Mean and variation of the proposed functional outcomes across subjects are then modeled using functional principal components analysis. Applications to data from a visual exploration paradigm conducted by the Autism Biomarkers Consortium for Clinical Trials showcase the novel insights gained from the proposed FDA approach, including significant group differences between children diagnosed with autism and their typically developing peers in their consistency of looking at faces early on in trial time.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"15 1","pages":"261-287"},"PeriodicalIF":1.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10112660/pdf/nihms-1842687.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9378156","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

Using Controlled Feeding Study for Biomarker Development in Regression Calibration for Disease Association Estimation. 疾病关联估计回归校准中生物标志物发展的控制饲养研究。

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Statistics in Biosciences Pub Date : 2023-04-01 DOI: 10.1007/s12561-022-09349-3

Cheng Zheng, Yiwen Zhang, Ying Huang, Ross Prentice

{"title":"Using Controlled Feeding Study for Biomarker Development in Regression Calibration for Disease Association Estimation.","authors":"Cheng Zheng, Yiwen Zhang, Ying Huang, Ross Prentice","doi":"10.1007/s12561-022-09349-3","DOIUrl":"https://doi.org/10.1007/s12561-022-09349-3","url":null,"abstract":"Correction for systematic measurement error in self-reported data is an important challenge in association studies of dietary intake and chronic disease risk. The regression calibration method has been used for this purpose when an objectively measured biomarker is available. However, a big limitation of the regression calibration method is that biomarkers have only been developed for a few dietary components. We propose new methods to use controlled feeding studies to develop valid biomarkers for many more dietary components and to estimate the diet disease associations. Asymptotic distribution theory for the proposed estimators is derived. Extensive simulation is performed to study the finite sample performance of the proposed estimators. We applied our method to examine the associations between the sodium/potassium intake ratio and cardiovascular disease incidence using the Women's Health Initiative cohort data. We discovered positive associations between sodium/potassium ratio and the risks of coronary heart disease, nonfatal myocardial infarction, coronary death, ischemic stroke, and total cardiovascular disease.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"15 1","pages":"57-113"},"PeriodicalIF":1.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270384/pdf/nihms-1888533.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10024335","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: 2

Causal Inference with Secondary Outcomes 与次要结果的因果推断

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Statistics in Biosciences Pub Date : 2023-03-20 DOI: 10.1007/s12561-023-09363-z

Ying Zhou

引用次数: 0

Introduction 介绍

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Statistics in Biosciences Pub Date : 2023-01-17 DOI: 10.1007/s12561-018-9218-3

Benjamin Gillespie

引用次数: 0