Biometrical Journal最新文献_第4页

Issue Information: Biometrical Journal 6'24 发行信息：生物计量学杂志 6'24

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-22 DOI: 10.1002/bimj.202470006

引用次数: 0

A Marginalized Zero-Inflated Negative Binomial Model for Spatial Data: Modeling COVID-19 Deaths in Georgia 空间数据的边际零膨胀负二项模型：佐治亚州 COVID-19 死亡建模。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-13 DOI: 10.1002/bimj.202300182

Fedelis Mutiso, John L. Pearce, Sara E. Benjamin-Neelon, Noel T. Mueller, Hong Li, Brian Neelon

{"title":"A Marginalized Zero-Inflated Negative Binomial Model for Spatial Data: Modeling COVID-19 Deaths in Georgia","authors":"Fedelis Mutiso, John L. Pearce, Sara E. Benjamin-Neelon, Noel T. Mueller, Hong Li, Brian Neelon","doi":"10.1002/bimj.202300182","DOIUrl":"10.1002/bimj.202300182","url":null,"abstract":"<div>\u0000 \u0000 Spatial count data with an abundance of zeros arise commonly in disease mapping studies. Typically, these data are analyzed using zero-inflated models, which comprise a mixture of a point mass at zero and an ordinary count distribution, such as the Poisson or negative binomial. However, due to their mixture representation, conventional zero-inflated models are challenging to explain in practice because the parameter estimates have conditional latent-class interpretations. As an alternative, several authors have proposed marginalized zero-inflated models that simultaneously model the excess zeros and the marginal mean, leading to a parameterization that more closely aligns with ordinary count models. Motivated by a study examining predictors of COVID-19 death rates, we develop a spatiotemporal marginalized zero-inflated negative binomial model that directly models the marginal mean, thus extending marginalized zero-inflated models to the spatial setting. To capture the spatiotemporal heterogeneity in the data, we introduce region-level covariates, smooth temporal effects, and spatially correlated random effects to model both the excess zeros and the marginal mean. For estimation, we adopt a Bayesian approach that combines full-conditional Gibbs sampling and Metropolis–Hastings steps. We investigate features of the model and use the model to identify key predictors of COVID-19 deaths in the US state of Georgia during the 2021 calendar year.</div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141604513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Years of Life Lost to COVID-19 and Related Mortality Indicators: An Illustration in 30 Countries COVID-19 及相关死亡率指标造成的生命损失年数：30 个国家的说明。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-13 DOI: 10.1002/bimj.202300386

Valentin Rousson, Isabella Locatelli

引用次数: 0

Robust Regression Techniques for Multiple Method Comparison and Transformation 用于多种方法比较和转换的稳健回归技术。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-13 DOI: 10.1002/bimj.202400027

Florian Dufey

引用次数: 0

A Shared-Frailty Spatial Scan Statistic Model for Time-to-Event Data 时间-事件数据的共享-弱点空间扫描统计模型。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-11 DOI: 10.1002/bimj.202300200

Camille Frévent, Mohamed-Salem Ahmed, Sophie Dabo-Niang, Michaël Genin

{"title":"A Shared-Frailty Spatial Scan Statistic Model for Time-to-Event Data","authors":"Camille Frévent, Mohamed-Salem Ahmed, Sophie Dabo-Niang, Michaël Genin","doi":"10.1002/bimj.202300200","DOIUrl":"10.1002/bimj.202300200","url":null,"abstract":"Spatial scan statistics are well-known methods widely used to detect spatial clusters of events. Furthermore, several spatial scan statistics models have been applied to the spatial analysis of time-to-event data. However, these models do not take account of potential correlations between the observations of individuals within the same spatial unit or potential spatial dependence between spatial units. To overcome this problem, we have developed a scan statistic based on a Cox model with shared frailty and that takes account of the spatial dependence between spatial units. In simulation studies, we found that (i) conventional models of spatial scan statistics for time-to-event data fail to maintain the type I error in the presence of a correlation between the observations of individuals within the same spatial unit and (ii) our model performed well in the presence of such correlation and spatial dependence. We have applied our method to epidemiological data and the detection of spatial clusters of mortality in patients with end-stage renal disease in northern France.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300200","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sample Size Calculation for an Individual Stepped-Wedge Randomized Trial 个人阶梯楔形随机试验的样本量计算。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-11 DOI: 10.1002/bimj.202300167

Aude Allemang-Trivalle, Annabel Maruani, Bruno Giraudeau

引用次数: 0

Biostatistical Aspects of Whole Genome Sequencing Studies: Preprocessing and Quality Control 全基因组测序研究的生物统计方面：预处理和质量控制

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-11 DOI: 10.1002/bimj.202300278

Raphael O. Betschart, Cristian Riccio, Domingo Aguilera-Garcia, Stefan Blankenberg, Linlin Guo, Holger Moch, Dagmar Seidl, Hugo Solleder, Felix Thalén, Alexandre Thiéry, Raphael Twerenbold, Tanja Zeller, Martin Zoche, Andreas Ziegler

{"title":"Biostatistical Aspects of Whole Genome Sequencing Studies: Preprocessing and Quality Control","authors":"Raphael O. Betschart, Cristian Riccio, Domingo Aguilera-Garcia, Stefan Blankenberg, Linlin Guo, Holger Moch, Dagmar Seidl, Hugo Solleder, Felix Thalén, Alexandre Thiéry, Raphael Twerenbold, Tanja Zeller, Martin Zoche, Andreas Ziegler","doi":"10.1002/bimj.202300278","DOIUrl":"10.1002/bimj.202300278","url":null,"abstract":"Rapid advances in high-throughput DNA sequencing technologies have enabled large-scale whole genome sequencing (WGS) studies. Before performing association analysis between phenotypes and genotypes, preprocessing and quality control (QC) of the raw sequence data need to be performed. Because many biostatisticians have not been working with WGS data so far, we first sketch Illumina's short-read sequencing technology. Second, we explain the general preprocessing pipeline for WGS studies. Third, we provide an overview of important QC metrics, which are applied to WGS data: on the raw data, after mapping and alignment, after variant calling, and after multisample variant calling. Fourth, we illustrate the QC with the data from the GENEtic SequencIng Study Hamburg–Davos (GENESIS-HD), a study involving more than 9000 human whole genomes. All samples were sequenced on an Illumina NovaSeq 6000 with an average coverage of 35× using a PCR-free protocol. For QC, one genome in a bottle (GIAB) trio was sequenced in four replicates, and one GIAB sample was successfully sequenced 70 times in different runs. Fifth, we provide empirical data on the compression of raw data using the DRAGEN original read archive (ORA). The most important quality metrics in the application were genetic similarity, sample cross-contamination, deviations from the expected Het/Hom ratio, relatedness, and coverage. The compression ratio of the raw files using DRAGEN ORA was 5.6:1, and compression time was linear by genome coverage. In summary, the preprocessing, joint calling, and QC of large WGS studies are feasible within a reasonable time, and efficient QC procedures are readily available.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300278","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Functional Multivariable Logistic Regression With an Application to HIV Viral Suppression Prediction 应用于 HIV 病毒抑制预测的功能性多变量 Logistic 回归。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-05 DOI: 10.1002/bimj.202300081

Siyuan Guo, Jiajia Zhang, Yichao Wu, Alexander C. McLain, James W. Hardin, Bankole Olatosi, Xiaoming Li

{"title":"Functional Multivariable Logistic Regression With an Application to HIV Viral Suppression Prediction","authors":"Siyuan Guo, Jiajia Zhang, Yichao Wu, Alexander C. McLain, James W. Hardin, Bankole Olatosi, Xiaoming Li","doi":"10.1002/bimj.202300081","DOIUrl":"10.1002/bimj.202300081","url":null,"abstract":"Motivated by improving the prediction of the human immunodeficiency virus (HIV) suppression status using electronic health records (EHR) data, we propose a functional multivariable logistic regression model, which accounts for the longitudinal binary process and continuous process simultaneously. Specifically, the longitudinal measurements for either binary or continuous variables are modeled by functional principal components analysis, and their corresponding functional principal component scores are used to build a logistic regression model for prediction. The longitudinal binary data are linked to underlying Gaussian processes. The estimation is done using penalized spline for the longitudinal continuous and binary data. Group-lasso is used to select longitudinal processes, and the multivariate functional principal components analysis is proposed to revise functional principal component scores with the correlation. The method is evaluated via comprehensive simulation studies and then applied to predict viral suppression using EHR data for people living with HIV in South Carolina.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combining Partial True Discovery Guarantee Procedures 结合部分真实发现保证程序。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-02 DOI: 10.1002/bimj.202300075

Ningning Xu, Aldo Solari, Jelle J. Goeman

{"title":"Combining Partial True Discovery Guarantee Procedures","authors":"Ningning Xu, Aldo Solari, Jelle J. Goeman","doi":"10.1002/bimj.202300075","DOIUrl":"10.1002/bimj.202300075","url":null,"abstract":"Closed testing has recently been shown to be optimal for simultaneous true discovery proportion control. It is, however, challenging to construct true discovery guarantee procedures in such a way that it focuses power on some feature sets chosen by users based on their specific interest or expertise. We propose a procedure that allows users to target power on prespecified feature sets, that is, “focus sets.” Still, the method also allows inference for feature sets chosen post hoc, that is, “nonfocus sets,” for which we deduce a true discovery lower confidence bound by interpolation. Our procedure is built from partial true discovery guarantee procedures combined with Holm's procedure and is a conservative shortcut to the closed testing procedure. A simulation study confirms that the statistical power of our method is relatively high for focus sets, at the cost of power for nonfocus sets, as desired. In addition, we investigate its power property for sets with specific structures, for example, trees and directed acyclic graphs. We also compare our method with AdaFilter in the context of replicability analysis. The application of our method is illustrated with a gene ontology analysis in gene expression data.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach 生物医学研究中多个二元终点的同时推断：多重边际模型的小样本特性和重采样方法。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-02 DOI: 10.1002/bimj.202300197

Sören Budig, Klaus Jung, Mario Hasler, Frank Schaarschmidt

{"title":"Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach","authors":"Sören Budig, Klaus Jung, Mario Hasler, Frank Schaarschmidt","doi":"10.1002/bimj.202300197","DOIUrl":"10.1002/bimj.202300197","url":null,"abstract":"In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step <math>\u0000 <semantics>\u0000 <mi>p</mi>\u0000 <annotation>$p$</annotation>\u0000 </semantics></math>-value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National Toxicology Program.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300197","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0