Statistics in Biosciences最新文献_第10页

A super scalable algorithm for short segment detection. 一种超可扩展的短段检测算法。

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Statistics in Biosciences Pub Date : 2021-04-01 Epub Date: 2020-04-18 DOI: 10.1007/s12561-020-09278-z

Ning Hao, Yue Selena Niu, Feifei Xiao, Heping Zhang

引用次数: 1

Regression analysis of mixed panel-count data with application to cancer studies. 混合面板计数数据在癌症研究中的应用的回归分析。

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Statistics in Biosciences Pub Date : 2021-04-01 Epub Date: 2020-08-17 DOI: 10.1007/s12561-020-09291-2

Yimei Li, Liang Zhu, Lei Liu, Leslie L Robison

引用次数: 1

A general approach to sensitivity analysis for Mendelian randomization. 孟德尔随机化敏感性分析的一般方法。

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Statistics in Biosciences Pub Date : 2021-04-01 Epub Date: 2020-04-28 DOI: 10.1007/s12561-020-09280-5

Weiming Zhang, Debashis Ghosh

{"title":"A general approach to sensitivity analysis for Mendelian randomization.","authors":"Weiming Zhang, Debashis Ghosh","doi":"10.1007/s12561-020-09280-5","DOIUrl":"https://doi.org/10.1007/s12561-020-09280-5","url":null,"abstract":"Mendelian Randomization (MR) represents a class of instrumental variable methods using genetic variants. It has become popular in epidemiological studies to account for the unmeasured confounders when estimating the effect of exposure on outcome. The success of Mendelian Randomization depends on three critical assumptions, which are difficult to verify. Therefore, sensitivity analysis methods are needed for evaluating results and making plausible conclusions. We propose a general and easy to apply approach to conduct sensitivity analysis for Mendelian Randomization studies. Bound et al. (1995) derived a formula for the asymptotic bias of the instrumental variable estimator. Based on their work, we derive a new sensitivity analysis formula. The parameters in the formula include sensitivity parameters such as the correlation between instruments and unmeasured confounder, the direct effect of instruments on outcome and the strength of instruments. In our simulation studies, we examined our approach in various scenarios using either individual SNPs or unweighted allele score as instruments. By using a previously published dataset from researchers involving a bone mineral density study, we demonstrate that our proposed method is a useful tool for MR studies, and that investigators can combine their domain knowledge with our method to obtain bias-corrected results and make informed conclusions on the scientific plausibility of their findings.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s12561-020-09280-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25504730","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: 3

Intergenerational Associations Between Maternal Diet and Childhood Adiposity: A Bayesian Regularized Mediation Analysis 母亲饮食与儿童肥胖的代际关联：贝叶斯正则中介分析

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Statistics in Biosciences Pub Date : 2021-03-21 DOI: 10.1007/s12561-021-09305-7

Yu-Bo Wang, Cuilin Zhang, Zhen Chen

引用次数: 1

Modeling Longitudinal Microbiome Compositional Data: A Two-Part Linear Mixed Model with Shared Random Effects 纵向微生物组组成数据建模:具有共享随机效应的两部分线性混合模型

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Statistics in Biosciences Pub Date : 2021-03-11 DOI: 10.1007/s12561-021-09302-w

Yongli Han, Courtney Baker, E. Vogtmann, X. Hua, Jianxin Shi, Danping Liu

引用次数: 1

Introduction to Special Issue on Statistics in Microbiome and Metagenomics 微生物组学和宏基因组学统计特刊导论

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Statistics in Biosciences Pub Date : 2021-03-10 DOI: 10.1007/s12561-021-09307-5

Huilin Li, Hongzhe Li

引用次数: 1

Spatial Weighted Analysis of Malnutrition Among Children in Nigeria: A Bayesian Approach 尼日利亚儿童营养不良的空间加权分析:贝叶斯方法

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Statistics in Biosciences Pub Date : 2021-03-05 DOI: 10.1007/s12561-021-09303-9

O. Egbon, Omodolapo Somo-Aina, E. Gayawan

引用次数: 8

Average Response over Time as Estimand: An Alternative Implementation of the While on Treatment Strategy 平均反应随时间的估计:一种替代实施的同时治疗策略

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Statistics in Biosciences Pub Date : 2021-02-18 DOI: 10.1007/s12561-021-09301-x

Naitee Ting, Lihong Huang, Q. Deng, J. Cappelleri

引用次数: 2

Positive Stable Shared Frailty Models Based on Additive Hazards 基于加性风险的正稳定共享脆弱性模型

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Statistics in Biosciences Pub Date : 2021-01-03 DOI: 10.1007/s12561-020-09299-8

David D. Hanagal

引用次数: 1

On a Statistical Transmission Model in Analysis of the Early Phase of COVID-19 Outbreak. 基于统计传播模型的新冠肺炎疫情早期分析

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Statistics in Biosciences Pub Date : 2021-01-01 Epub Date: 2020-04-02 DOI: 10.1007/s12561-020-09277-0

Yifan Zhu, Ying Qing Chen

{"title":"On a Statistical Transmission Model in Analysis of the Early Phase of COVID-19 Outbreak.","authors":"Yifan Zhu, Ying Qing Chen","doi":"10.1007/s12561-020-09277-0","DOIUrl":"https://doi.org/10.1007/s12561-020-09277-0","url":null,"abstract":"Since December 2019, a disease caused by a novel strain of coronavirus (COVID-19) had infected many people and the cumulative confirmed cases have reached almost 180,000 as of 17, March 2020. The COVID-19 outbreak was believed to have emerged from a seafood market in Wuhan, a metropolis city of more than 11 million population in Hubei province, China. We introduced a statistical disease transmission model using case symptom onset data to estimate the transmissibility of the early-phase outbreak in China, and provided sensitivity analyses with various assumptions of disease natural history of the COVID-19. We fitted the transmission model to several publicly available sources of the outbreak data until 11, February 2020, and estimated lock down intervention efficacy of Wuhan city. The estimated <math><msub><mi>R</mi> <mn>0</mn></msub> </math> was between 2.7 and 4.2 from plausible distribution assumptions of the incubation period and relative infectivity over the infectious period. 95% confidence interval of <math><msub><mi>R</mi> <mn>0</mn></msub> </math> were also reported. Potential issues such as data quality concerns and comparison of different modelling approaches were discussed.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s12561-020-09277-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37836121","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: 52