International Journal of Biostatistics最新文献

Comments on “sensitivity of estimands in clinical trials with imperfect compliance” by Chen and Heitjan 对 Chen 和 Heitjan 的 "不完全依从性临床试验中估计值的敏感性 "的评论

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2024-07-27 DOI: 10.1515/ijb-2023-0127

Stuart G. Baker, Karen S. Lindeman

引用次数: 0

Detecting differentially expressed genes from RNA-seq data using fuzzy clustering 利用模糊聚类从 RNA-seq 数据中检测差异表达基因

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2024-07-27 DOI: 10.1515/ijb-2023-0125

Yuki Ando, Asanao Shimokawa

{"title":"Detecting differentially expressed genes from RNA-seq data using fuzzy clustering","authors":"Yuki Ando, Asanao Shimokawa","doi":"10.1515/ijb-2023-0125","DOIUrl":"https://doi.org/10.1515/ijb-2023-0125","url":null,"abstract":"A two-group comparison test is generally performed on RNA sequencing data to detect differentially expressed genes (DEGs). However, the accuracy of this method is low due to the small sample size. To address this, we propose a method using fuzzy clustering that artificially generates data with expression patterns similar to those of DEGs to identify genes that are highly likely to be classified into the same cluster as the initial cluster data. The proposed method is advantageous in that it does not perform any test. Furthermore, a certain level of accuracy can be maintained even when the sample size is biased, and we show that such a situation may improve the accuracy of the proposed method. We compared the proposed method with the conventional method using simulations. In the simulations, we changed the sample size and difference between the expression levels of group 1 and group 2 in the DEGs to obtain the desired accuracy of the proposed method. The results show that the proposed method is superior in all cases under the conditions simulated. We also show that the effect of the difference between group 1 and group 2 on the accuracy is more prominent when the sample size is biased.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Random forests for survival data: which methods work best and under what conditions? 生存数据的随机森林：哪些方法在哪些条件下最有效？

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2024-04-24 DOI: 10.1515/ijb-2023-0056

Matthew Berkowitz, Rachel MacKay Altman, Thomas M. Loughin

引用次数: 0

Kalman filter with impulse noised outliers: a robust sequential algorithm to filter data with a large number of outliers 具有脉冲噪声离群值的卡尔曼滤波器：过滤大量离群值数据的稳健顺序算法

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2024-04-16 DOI: 10.1515/ijb-2023-0065

Bertrand Cloez, Bénédicte Fontez, Eliel González-García, Isabelle Sanchez

引用次数: 0

The survival function NPMLE for combined right-censored and length-biased right-censored failure time data: properties and applications 综合右删失和长度偏右删失故障时间数据的生存函数 NPMLE：特性与应用

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2024-04-09 DOI: 10.1515/ijb-2023-0121

James H. McVittie, David B. Wolfson, David A. Stephens

引用次数: 0

Ensemble learning methods of inference for spatially stratified infectious disease systems 空间分层传染病系统推理的集合学习方法

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2024-04-09 DOI: 10.1515/ijb-2023-0102

Jeffrey Peitsch, Gyanendra Pokharel, Shakhawat Hossain

{"title":"Ensemble learning methods of inference for spatially stratified infectious disease systems","authors":"Jeffrey Peitsch, Gyanendra Pokharel, Shakhawat Hossain","doi":"10.1515/ijb-2023-0102","DOIUrl":"https://doi.org/10.1515/ijb-2023-0102","url":null,"abstract":"Individual level models are a class of mechanistic models that are widely used to infer infectious disease transmission dynamics. These models incorporate individual level covariate information accounting for population heterogeneity and are generally fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework. However, Bayesian MCMC methods of inference are computationally expensive for large data sets. This issue becomes more severe when applied to infectious disease data collected from spatially heterogeneous populations, as the number of covariates increases. In addition, summary statistics over the global population may not capture the true spatio-temporal dynamics of disease transmission. In this study we propose to use ensemble learning methods to predict epidemic generating models instead of time consuming Bayesian MCMC method. We apply these methods to infer disease transmission dynamics over spatially clustered populations, considering the clusters as natural strata instead of a global population. We compare the performance of two tree-based ensemble learning techniques: random forest and gradient boosting. These methods are applied to the 2001 foot-and-mouth disease epidemic in the U.K. and evaluated using simulated data from a clustered population. It is shown that the spatially clustered data can help to predict epidemic generating models more accurately than the global data.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MBPCA-OS: an exploratory multiblock method for variables of different measurement levels. Application to study the immune response to SARS-CoV-2 infection and vaccination MBPCA-OS：针对不同测量水平变量的探索性多块方法。应用于研究 SARS-CoV-2 感染和疫苗接种的免疫反应

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2023-12-12 DOI: 10.1515/ijb-2023-0062

Martin Paries, Evelyne Vigneau, Adeline Huneau, Olivier Lantz, Stéphanie Bougeard

{"title":"MBPCA-OS: an exploratory multiblock method for variables of different measurement levels. Application to study the immune response to SARS-CoV-2 infection and vaccination","authors":"Martin Paries, Evelyne Vigneau, Adeline Huneau, Olivier Lantz, Stéphanie Bougeard","doi":"10.1515/ijb-2023-0062","DOIUrl":"https://doi.org/10.1515/ijb-2023-0062","url":null,"abstract":"Studying a large number of variables measured on the same observations and organized in blocks – denoted multiblock data – is becoming standard in several domains especially in biology. To explore the relationships between all these variables – at the block- and the variable-level – several exploratory multiblock methods were proposed. However, most of them are only designed for numeric variables. In reality, some data sets contain variables of different measurement levels (i.e., numeric, nominal, ordinal). In this article, we focus on exploratory multiblock methods that handle variables at their appropriate measurement level. Multi-Block Principal Component Analysis with Optimal Scaling (MBPCA-OS) is proposed and applied to multiblock data from the CURIE-O-SA French cohort. In this study, variables are of different measurement levels and organized in four blocks. The objective is to study the immune responses according to the SARS-CoV-2 infection and vaccination statuses, the symptoms and the participant’s characteristics.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138579777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0