Pub Date : 2021-10-23DOI: 10.1007/s12561-021-09327-1
Qingyang Liu, J. Geng, F. Fleischer, Q. Deng
{"title":"Efficacy-Driven Dose Finding with Toxicity Control in Phase I Oncology Studies","authors":"Qingyang Liu, J. Geng, F. Fleischer, Q. Deng","doi":"10.1007/s12561-021-09327-1","DOIUrl":"https://doi.org/10.1007/s12561-021-09327-1","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"14 1","pages":"413 - 431"},"PeriodicalIF":1.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47633278","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 : 2021-10-15DOI: 10.1007/s12561-021-09324-4
Long Wang, Fangzheng Xie, Yanxun Xu
{"title":"Simultaneous Learning the Dimension and Parameter of a Statistical Model with Big Data","authors":"Long Wang, Fangzheng Xie, Yanxun Xu","doi":"10.1007/s12561-021-09324-4","DOIUrl":"https://doi.org/10.1007/s12561-021-09324-4","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47383406","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 : 2021-10-08DOI: 10.1007/s12561-021-09325-3
D. Ghosh, M. Sabel
{"title":"A Weighted Sample Framework to Incorporate External Calculators for Risk Modeling","authors":"D. Ghosh, M. Sabel","doi":"10.1007/s12561-021-09325-3","DOIUrl":"https://doi.org/10.1007/s12561-021-09325-3","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"14 1","pages":"363 - 379"},"PeriodicalIF":1.0,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49257052","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 : 2021-10-01DOI: 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}
Pub Date : 2021-09-18DOI: 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}
Pub Date : 2021-08-09DOI: 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}
Pub Date : 2021-07-01DOI: 10.1007/s12561-020-09294-z
Jing Ma
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.
{"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}
Pub Date : 2021-05-15DOI: 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}
Pub Date : 2021-04-01Epub Date: 2020-04-18DOI: 10.1007/s12561-020-09278-z
Ning Hao, Yue Selena Niu, Feifei Xiao, Heping Zhang
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.
{"title":"A super scalable algorithm for short segment detection.","authors":"Ning Hao, Yue Selena Niu, Feifei Xiao, 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}
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