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A Model-Based Approach to Assess Epidemic Risk. 基于模型的流行病风险评估方法。
IF 0.8
Statistics in Biosciences Pub Date : 2022-01-01 Epub Date: 2021-11-15 DOI: 10.1007/s12561-021-09329-z
Hugo Dolan, Riccardo Rastelli
{"title":"A Model-Based Approach to Assess Epidemic Risk.","authors":"Hugo Dolan, Riccardo Rastelli","doi":"10.1007/s12561-021-09329-z","DOIUrl":"10.1007/s12561-021-09329-z","url":null,"abstract":"<p><p>We study how international flights can facilitate the spread of an epidemic to a worldwide scale. We combine an infrastructure network of flight connections with a population density dataset to derive the mobility network, and then we define an epidemic framework to model the spread of the disease. Our approach combines a compartmental SEIRS model with a graph diffusion model to capture the clusteredness of the distribution of the population. The resulting model is characterised by the dynamics of a metapopulation SEIRS, with amplification or reduction of the infection rate which is determined also by the mobility of individuals. We use simulations to characterise and study a variety of realistic scenarios that resemble the recent spread of COVID-19. Crucially, we define a formal framework that can be used to design epidemic mitigation strategies: we propose an optimisation approach based on genetic algorithms that can be used to identify an optimal airport closure strategy, and that can be employed to aid decision making for the mitigation of the epidemic, in a timely manner.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8591322/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39644376","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
Augmenting Both Arms of a Randomized Controlled Trial Using External Data: An Application of the Propensity Score-Integrated Approaches. 利用外部数据增强随机对照试验的两个分支:倾向得分综合方法的应用。
IF 1
Statistics in Biosciences Pub Date : 2022-01-01 Epub Date: 2021-06-19 DOI: 10.1007/s12561-021-09315-5
Heng Li, Wei-Chen Chen, Chenguang Wang, Nelson Lu, Changhong Song, Ram Tiwari, Yunling Xu, Lilly Q Yue
{"title":"Augmenting Both Arms of a Randomized Controlled Trial Using External Data: An Application of the Propensity Score-Integrated Approaches.","authors":"Heng Li,&nbsp;Wei-Chen Chen,&nbsp;Chenguang Wang,&nbsp;Nelson Lu,&nbsp;Changhong Song,&nbsp;Ram Tiwari,&nbsp;Yunling Xu,&nbsp;Lilly Q Yue","doi":"10.1007/s12561-021-09315-5","DOIUrl":"https://doi.org/10.1007/s12561-021-09315-5","url":null,"abstract":"<p><p>Leveraging external data is a topic that have recently received much attention. The propensity score-integrated approaches are a methodological innovation for this purpose. In this paper we adapt these approaches, originally introduced to augment single-arm studies with external data, for the augmentation of both arms of a randomized controlled trial (RCT) with external data. After recapitulating the basic ideas, we provide a step-by-step tutorial of how to implement the propensity score-integrated approaches, from study design to outcome analysis, in the RCT setting in such a way that the study integrity and objectively are maintained. Both the Bayesian (power prior) approach and the frequentist (composite likelihood) approach are included. Some extensions and variations of these approaches are also outlined at the end of this paper.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s12561-021-09315-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39133259","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: 6
A New Bayesian Two-Sample t Test and Solution to the Behrens–Fisher Problem Based on Gaussian Mixture Modelling with Known Allocations 一种新的贝叶斯双样本t检验及基于已知分配的高斯混合模型的Behrens–Fisher问题的求解
IF 1
Statistics in Biosciences Pub Date : 2021-12-10 DOI: 10.1007/s12561-021-09326-2
Riko Kelter
{"title":"A New Bayesian Two-Sample t Test and Solution to the Behrens–Fisher Problem Based on Gaussian Mixture Modelling with Known Allocations","authors":"Riko Kelter","doi":"10.1007/s12561-021-09326-2","DOIUrl":"https://doi.org/10.1007/s12561-021-09326-2","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45797212","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
Correction to: Sample Size Re-estimation with the Com-Nougue Method to Evaluate Treatment Effect 修正:用como - nougue法重新估计样本量以评价治疗效果
IF 1
Statistics in Biosciences Pub Date : 2021-12-09 DOI: 10.1007/s12561-021-09333-3
Jin Wang
{"title":"Correction to: Sample Size Re-estimation with the Com-Nougue Method to Evaluate Treatment Effect","authors":"Jin Wang","doi":"10.1007/s12561-021-09333-3","DOIUrl":"https://doi.org/10.1007/s12561-021-09333-3","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"52603334","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 Modeling of Geometric Features of Longitudinal Process and Discrete Survival Time Measured on Nested Timescales: An Application to Fecundity Studies 纵向过程几何特征和嵌套时间尺度上的离散生存时间联合建模:在繁殖力研究中的应用
IF 1
Statistics in Biosciences Pub Date : 2021-12-06 DOI: 10.1007/s12561-023-09381-x
Abhisek Saha, Ling Ma, A. Biswas, R. Sundaram
{"title":"Joint Modeling of Geometric Features of Longitudinal Process and Discrete Survival Time Measured on Nested Timescales: An Application to Fecundity Studies","authors":"Abhisek Saha, Ling Ma, A. Biswas, R. Sundaram","doi":"10.1007/s12561-023-09381-x","DOIUrl":"https://doi.org/10.1007/s12561-023-09381-x","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46399657","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
A Statistical Method for Association Analysis of Cell Type Compositions. 细胞类型组成关联分析的统计方法。
IF 1
Statistics in Biosciences Pub Date : 2021-12-01 Epub Date: 2021-09-15 DOI: 10.1007/s12561-020-09293-0
Licai Huang, Paul Little, Jeroen R Huyghe, Qian Shi, Tabitha A Harrison, Greg Yothers, Thomas J George, Ulrike Peters, Andrew T Chan, Polly A Newcomb, Wei Sun
{"title":"A Statistical Method for Association Analysis of Cell Type Compositions.","authors":"Licai Huang,&nbsp;Paul Little,&nbsp;Jeroen R Huyghe,&nbsp;Qian Shi,&nbsp;Tabitha A Harrison,&nbsp;Greg Yothers,&nbsp;Thomas J George,&nbsp;Ulrike Peters,&nbsp;Andrew T Chan,&nbsp;Polly A Newcomb,&nbsp;Wei Sun","doi":"10.1007/s12561-020-09293-0","DOIUrl":"10.1007/s12561-020-09293-0","url":null,"abstract":"<p><p>Gene expression data are often collected from tissue samples that are composed of multiple cell types. Studies of cell type composition based on gene expression data from tissue samples have recently attracted increasing research interest and led to new method development for cell type composition estimation. This new information on cell type composition can be associated with individual characteristics (e.g., genetic variants) or clinical outcomes (e.g., survival time). Such association analysis can be conducted for each cell type separately followed by multiple testing correction. An alternative approach is to evaluate this association using the composition of all the cell types, thus aggregating association signals across cell types. A key challenge of this approach is to account for the dependence across cell types. We propose a new method to quantify the distances between cell types while accounting for their dependencies, and use this information for association analysis. We demonstrate our method in two applied examples: to assess the association between immune cell type composition in tumor samples of colorectal cancer patients versus survival time and SNP genotypes. We found immune cell composition has prognostic value, and our distance metric leads to more accurate survival time prediction than other distance metrics that ignore cell type dependencies. In addition, survival time-associated SNPs are enriched among the SNPs associated with immune cell composition.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s12561-020-09293-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10319800","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 Unified Decision Framework for Phase I Dose-Finding Designs 一期剂量寻找设计的统一决策框架
IF 1
Statistics in Biosciences Pub Date : 2021-11-24 DOI: 10.1007/s12561-023-09379-5
Yunshan Duan, Shijie Yuan, Yuan Ji, Peter Mueller
{"title":"A Unified Decision Framework for Phase I Dose-Finding Designs","authors":"Yunshan Duan, Shijie Yuan, Yuan Ji, Peter Mueller","doi":"10.1007/s12561-023-09379-5","DOIUrl":"https://doi.org/10.1007/s12561-023-09379-5","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48870494","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
Epistasis Detection via the Joint Cumulant 通过联合积存量检测上溢
IF 1
Statistics in Biosciences Pub Date : 2021-11-12 DOI: 10.1007/s12561-022-09336-8
Randall Reese, G. Fu, Geran Zhao, Xiaotian Dai, Xiaotian Li, K. Chiu
{"title":"Epistasis Detection via the Joint Cumulant","authors":"Randall Reese, G. Fu, Geran Zhao, Xiaotian Dai, Xiaotian Li, K. Chiu","doi":"10.1007/s12561-022-09336-8","DOIUrl":"https://doi.org/10.1007/s12561-022-09336-8","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47939585","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
A Power Prior Approach for Leveraging External Longitudinal and Competing Risks Survival Data Within the Joint Modeling Framework 在联合建模框架内利用外部纵向和竞争风险生存数据的幂优先方法
IF 1
Statistics in Biosciences Pub Date : 2021-11-06 DOI: 10.1007/s12561-021-09330-6
Md. Tuhin Sheikh, Ming-Hui Chen, J. Gelfond, J. Ibrahim
{"title":"A Power Prior Approach for Leveraging External Longitudinal and Competing Risks Survival Data Within the Joint Modeling Framework","authors":"Md. Tuhin Sheikh, Ming-Hui Chen, J. Gelfond, J. Ibrahim","doi":"10.1007/s12561-021-09330-6","DOIUrl":"https://doi.org/10.1007/s12561-021-09330-6","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43092140","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
Efficacy-Driven Dose Finding with Toxicity Control in Phase I Oncology Studies 在I期肿瘤研究中，疗效驱动的剂量发现和毒性控制
IF 1
Statistics in Biosciences Pub Date : 2021-10-23 DOI: 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":null,"pages":null},"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}
引用次数: 0
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