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Leveraging Natural History Data in One- and Two-Arm Hierarchical Bayesian Studies of Rare Disease Progression 利用自然史数据进行罕见病进展的单臂和双臂层次贝叶斯研究
IF 1
Statistics in Biosciences Pub Date : 2021-10-01 DOI: 10.1007/s12561-021-09323-5
A. Monseur, B. Carlin, B. Boulanger, A. Seferian, L. Servais, Chris Freitag, L. Thielemans, Teresa Elena Virginie Ulrike Andrea Adele James J. Basil Gidaro Gargaun Chê Schara Gangfuß D’Amico Dowling , T. Gidaro, E. Gargaun, V. Chê, U. Schara, A. Gangfuss, A. D’Amico, J. Dowling, B. Darras, A. Daron, Arturo E. Hernandez, C. de Lattre, J. Arnal, Michèle Mayer, J. Cuisset, C. Vuillerot, S. Fontaine, R. Bellance, V. Biancalana, A. Buj-Bello, J. Hogrel, H. Landy, K. Amburgey, B. Andres, E. Bertini, R. Cardaş, S. Denis, Dominique Duchêne, V. Latournerie, Nacera Reguiba, E. Tsuchiya, C. Wallgren‐Pettersson
{"title":"Leveraging Natural History Data in One- and Two-Arm Hierarchical Bayesian Studies of Rare Disease Progression","authors":"A. Monseur, B. Carlin, B. Boulanger, A. Seferian, L. Servais, Chris Freitag, L. Thielemans, Teresa Elena Virginie Ulrike Andrea Adele James J. Basil Gidaro Gargaun Chê Schara Gangfuß D’Amico Dowling , T. Gidaro, E. Gargaun, V. Chê, U. Schara, A. Gangfuss, A. D’Amico, J. Dowling, B. Darras, A. Daron, Arturo E. Hernandez, C. de Lattre, J. Arnal, Michèle Mayer, J. Cuisset, C. Vuillerot, S. Fontaine, R. Bellance, V. Biancalana, A. Buj-Bello, J. Hogrel, H. Landy, K. Amburgey, B. Andres, E. Bertini, R. Cardaş, S. Denis, Dominique Duchêne, V. Latournerie, Nacera Reguiba, E. Tsuchiya, C. Wallgren‐Pettersson","doi":"10.1007/s12561-021-09323-5","DOIUrl":"https://doi.org/10.1007/s12561-021-09323-5","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"14 1","pages":"237 - 258"},"PeriodicalIF":1.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42161032","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: 1
Flexible Conditional Borrowing Approaches for Leveraging Historical Data in the Bayesian Design of Superiority Trials 在优势试验的贝叶斯设计中利用历史数据的灵活条件借用方法
IF 1
Statistics in Biosciences Pub Date : 2021-09-18 DOI: 10.1007/s12561-021-09321-7
Weiying Yuan, Ming-Hui Chen, J. Zhong
{"title":"Flexible Conditional Borrowing Approaches for Leveraging Historical Data in the Bayesian Design of Superiority Trials","authors":"Weiying Yuan, Ming-Hui Chen, J. Zhong","doi":"10.1007/s12561-021-09321-7","DOIUrl":"https://doi.org/10.1007/s12561-021-09321-7","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"14 1","pages":"197 - 215"},"PeriodicalIF":1.0,"publicationDate":"2021-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46761465","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: 1
Bayesian Analysis of Multivariate Matched Proportions with Sparse Response 具有稀疏响应的多元匹配比例的贝叶斯分析
IF 1
Statistics in Biosciences Pub Date : 2021-08-09 DOI: 10.1007/s12561-023-09368-8
M. Meyer, Hao Cheng, K. Knutson
{"title":"Bayesian Analysis of Multivariate Matched Proportions with Sparse Response","authors":"M. Meyer, Hao Cheng, K. Knutson","doi":"10.1007/s12561-023-09368-8","DOIUrl":"https://doi.org/10.1007/s12561-023-09368-8","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"15 1","pages":"490 - 509"},"PeriodicalIF":1.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48568588","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
Joint Microbial and Metabolomic Network Estimation with the Censored Gaussian Graphical Model. 用删节高斯图模型估计微生物和代谢组学联合网络。
IF 1
Statistics in Biosciences Pub Date : 2021-07-01 DOI: 10.1007/s12561-020-09294-z
Jing Ma
{"title":"Joint Microbial and Metabolomic Network Estimation with the Censored Gaussian Graphical Model.","authors":"Jing Ma","doi":"10.1007/s12561-020-09294-z","DOIUrl":"https://doi.org/10.1007/s12561-020-09294-z","url":null,"abstract":"<p><p>Joint analysis of microbiome and metabolomic data represents an imperative objective as the field moves beyond basic microbiome association studies and turns towards mechanistic and translational investigations. We present a censored Gaussian graphical model framework, where the metabolomic data are treated as continuous and the microbiome data as censored at zero, to identify direct interactions (defined as conditional dependence relationships) between microbial species and metabolites. Simulated examples show that our method metaMint performs favorably compared to the existing ones. metaMint also provides interpretable microbe-metabolite interactions when applied to a bacterial vaginosis data set. R implementation of metaMint is available on GitHub.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"13 2","pages":"351-372"},"PeriodicalIF":1.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s12561-020-09294-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10743117","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: 1
Distance-Based Analysis with Quantile Regression Models. 基于距离的分位数回归模型分析。
IF 1
Statistics in Biosciences Pub Date : 2021-07-01 Epub Date: 2021-03-27 DOI: 10.1007/s12561-021-09306-6
Shaoyu Li, Yanqing Sun, Liyang Diao, Xue Wang
{"title":"Distance-Based Analysis with Quantile Regression Models.","authors":"Shaoyu Li,&nbsp;Yanqing Sun,&nbsp;Liyang Diao,&nbsp;Xue Wang","doi":"10.1007/s12561-021-09306-6","DOIUrl":"https://doi.org/10.1007/s12561-021-09306-6","url":null,"abstract":"<p><p>Non-standard structured, multivariate data are emerging in many research areas, including genetics and genomics, ecology, and social science. Suitably defined pairwise distance measures are commonly used in distance-based analysis to study the association between the variables. In this work, we consider a linear quantile regression model for pairwise distances. We investigate the large sample properties of an estimator of the unknown coefficients and propose statistical inference procedures correspondingly. Extensive simulations provide evidence of satisfactory finite sample properties of the proposed method. Finally, we applied the method to a microbiome association study to illustrate its utility.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":"291-312"},"PeriodicalIF":1.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s12561-021-09306-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40601803","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
Estimating Heterogeneous Treatment Effect on Multivariate Responses Using Random Forests 利用随机森林估计异质处理对多变量响应的影响
IF 1
Statistics in Biosciences Pub Date : 2021-05-15 DOI: 10.1007/S12561-021-09310-W
Boyi Guo, H. Holscher, L. Auvil, M. Welge, C. Bushell, J. Novotny, D. Baer, N. Burd, Naiman A. Khan, Ruoqing Zhu
{"title":"Estimating Heterogeneous Treatment Effect on Multivariate Responses Using Random Forests","authors":"Boyi Guo, H. Holscher, L. Auvil, M. Welge, C. Bushell, J. Novotny, D. Baer, N. Burd, Naiman A. Khan, Ruoqing Zhu","doi":"10.1007/S12561-021-09310-W","DOIUrl":"https://doi.org/10.1007/S12561-021-09310-W","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"1 1","pages":"1-17"},"PeriodicalIF":1.0,"publicationDate":"2021-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/S12561-021-09310-W","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49077447","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: 3
Bayesian Joint Modeling of Single-Cell Expression Data and Bulk Spatial Transcriptomic Data 单细胞表达数据和海量空间转录组数据的贝叶斯联合建模
IF 1
Statistics in Biosciences Pub Date : 2021-04-12 DOI: 10.1007/S12561-021-09308-4
Jinge Yu, Qiuyu Wu, Xiangyu Luo
{"title":"Bayesian Joint Modeling of Single-Cell Expression Data and Bulk Spatial Transcriptomic Data","authors":"Jinge Yu, Qiuyu Wu, Xiangyu Luo","doi":"10.1007/S12561-021-09308-4","DOIUrl":"https://doi.org/10.1007/S12561-021-09308-4","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"1 1","pages":"1-15"},"PeriodicalIF":1.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/S12561-021-09308-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48166554","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: 1
A super scalable algorithm for short segment detection. 一种超可扩展的短段检测算法。
IF 1
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
{"title":"A super scalable algorithm for short segment detection.","authors":"Ning Hao,&nbsp;Yue Selena Niu,&nbsp;Feifei Xiao,&nbsp;Heping Zhang","doi":"10.1007/s12561-020-09278-z","DOIUrl":"https://doi.org/10.1007/s12561-020-09278-z","url":null,"abstract":"<p><p>In many applications such as copy number variant (CNV) detection, the goal is to identify short segments on which the observations have different means or medians from the background. Those segments are usually short and hidden in a long sequence, and hence are very challenging to find. We study a super scalable short segment (4S) detection algorithm in this paper. This nonparametric method clusters the locations where the observations exceed a threshold for segment detection. It is computationally efficient and does not rely on Gaussian noise assumption. Moreover, we develop a framework to assign significance levels for detected segments. We demonstrate the advantages of our proposed method by theoretical, simulation, and real data studies.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"13 1","pages":"18-33"},"PeriodicalIF":1.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s12561-020-09278-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25504729","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: 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。
IF 1
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
{"title":"Regression analysis of mixed panel-count data with application to cancer studies.","authors":"Yimei Li,&nbsp;Liang Zhu,&nbsp;Lei Liu,&nbsp;Leslie L Robison","doi":"10.1007/s12561-020-09291-2","DOIUrl":"https://doi.org/10.1007/s12561-020-09291-2","url":null,"abstract":"<p><p>Both panel-count data and panel-binary data are common data types in recurrent event studies. Because of inconsistent questionnaires or missing data during the follow-ups, mixed data types need to be addressed frequently. A recently proposed semiparametric approach uses a proportional means model to facilitate regression analyses of mixed panel-count and panel-binary data. This method can use all available information regardless of the record type and provide unbiased estimates. However, the large number of nuisance parameters in the nonparametric baseline hazard function makes the estimating procedure very complicated and time-consuming. We approximated the baseline hazard function to simplify the estimating procedure. Simulation studies showed that our method performed similarly to that of the previous semiparametric likelihood-based method, but with much faster speed. Approximating the baseline hazard not only reduced the computational burden but also made it possible to implement the estimating procedure in a standard software, such as SAS.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"13 1","pages":"178-195"},"PeriodicalIF":1.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s12561-020-09291-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25500982","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: 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。
IF 0.8
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":"10.1007/s12561-020-09280-5","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"13 1","pages":"34-55"},"PeriodicalIF":0.8,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962901/pdf/nihms-1588779.pdf","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: 0
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