Biometrical Journal最新文献_第2页

A Flexible Adaptive Lasso Cox Frailty Model Based on the Full Likelihood 基于全概率的灵活自适应拉索考克斯虚弱模型

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-10-08 DOI: 10.1002/bimj.202300020

Maike Hohberg, Andreas Groll

引用次数: 0

Testing for Sufficient Follow-Up in Censored Survival Data by Using Extremes 利用极值测试删失生存数据的充分随访性

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-10-08 DOI: 10.1002/bimj.202400033

Ping Xie, Mikael Escobar-Bach, Ingrid Van Keilegom

引用次数: 0

Meta-Analysis of Diagnostic Accuracy Studies With Multiple Thresholds: Comparison of Approaches in a Simulation Study 多阈值诊断准确性研究的元分析：模拟研究中各种方法的比较

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-09-27 DOI: 10.1002/bimj.202300101

Antonia Zapf, Cornelia Frömke, Juliane Hardt, Gerta Rücker, Dina Voeltz, Annika Hoyer

{"title":"Meta-Analysis of Diagnostic Accuracy Studies With Multiple Thresholds: Comparison of Approaches in a Simulation Study","authors":"Antonia Zapf, Cornelia Frömke, Juliane Hardt, Gerta Rücker, Dina Voeltz, Annika Hoyer","doi":"10.1002/bimj.202300101","DOIUrl":"https://doi.org/10.1002/bimj.202300101","url":null,"abstract":"The development of methods for the meta-analysis of diagnostic test accuracy (DTA) studies is still an active area of research. While methods for the standard case where each study reports a single pair of sensitivity and specificity are nearly routinely applied nowadays, methods to meta-analyze receiver operating characteristic (ROC) curves are not widely used. This situation is more complex, as each primary DTA study may report on several pairs of sensitivity and specificity, each corresponding to a different threshold. In a case study published earlier, we applied a number of methods for meta-analyzing DTA studies with multiple thresholds to a real-world data example (Zapf et al., Biometrical Journal. 2021; 63(4): 699–711). To date, no simulation study exists that systematically compares different approaches with respect to their performance in various scenarios when the truth is known. In this article, we aim to fill this gap and present the results of a simulation study that compares three frequentist approaches for the meta-analysis of ROC curves. We performed a systematic simulation study, motivated by an example from medical research. In the simulations, all three approaches worked partially well. The approach by Hoyer and colleagues was slightly superior in most scenarios and is recommended in practice.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324593","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

Post-Estimation Shrinkage in Full and Selected Linear Regression Models in Low-Dimensional Data Revisited 再论低维数据中完全线性回归模型和选定线性回归模型的估计后收缩率

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-09-27 DOI: 10.1002/bimj.202300368

Edwin Kipruto, Willi Sauerbrei

{"title":"Post-Estimation Shrinkage in Full and Selected Linear Regression Models in Low-Dimensional Data Revisited","authors":"Edwin Kipruto, Willi Sauerbrei","doi":"10.1002/bimj.202300368","DOIUrl":"https://doi.org/10.1002/bimj.202300368","url":null,"abstract":"The fit of a regression model to new data is often worse due to overfitting. Analysts use variable selection techniques to develop parsimonious regression models, which may introduce bias into regression estimates. Shrinkage methods have been proposed to mitigate overfitting and reduce bias in estimates. Post-estimation shrinkage is an alternative to penalized methods. This study evaluates effectiveness of post-estimation shrinkage in improving prediction performance of full and selected models. Through a simulation study, results were compared with ordinary least squares (OLS) and ridge in full models, and best subset selection (BSS) and lasso in selected models. We focused on prediction errors and the number of selected variables. Additionally, we proposed a modified version of the parameter-wise shrinkage (PWS) approach named non-negative PWS (NPWS) to address weaknesses of PWS. Results showed that no method was superior in all scenarios. In full models, NPWS outperformed global shrinkage, whereas PWS was inferior to OLS. In low correlation with moderate-to-high signal-to-noise ratio (SNR), NPWS outperformed ridge, but ridge performed best in small sample sizes, high correlation, and low SNR. In selected models, all post-estimation shrinkage performed similarly, with global shrinkage slightly inferior. Lasso outperformed BSS and post-estimation shrinkage in small sample sizes, low SNR, and high correlation but was inferior when the opposite was true. Our study suggests that, with sufficient information, NPWS is more effective than global shrinkage in improving prediction accuracy of models. However, in high correlation, small sample sizes, and low SNR, penalized methods generally outperform post-estimation shrinkage methods.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300368","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324583","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 Data Analysis: An Introduction and Recent Developments 功能数据分析：导论与最新发展

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-09-27 DOI: 10.1002/bimj.202300363

Jan Gertheiss, David Rügamer, Bernard X. W. Liew, Sonja Greven

{"title":"Functional Data Analysis: An Introduction and Recent Developments","authors":"Jan Gertheiss, David Rügamer, Bernard X. W. Liew, Sonja Greven","doi":"10.1002/bimj.202300363","DOIUrl":"https://doi.org/10.1002/bimj.202300363","url":null,"abstract":"Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar-valued or multivariate data, but FDA brings additional challenges due to the high- and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field. The paper covers fundamental concepts such as descriptives and outliers, smoothing, amplitude and phase variation, and functional principal component analysis. It also discusses functional regression, statistical inference with functional data, functional classification and clustering, and machine learning approaches for functional data analysis. The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data. Sparse functional data methods are also relevant for longitudinal data analysis. All presented methods are demonstrated using available software in R by analyzing a dataset on human motion and motor control. To facilitate the understanding of the methods, their implementation, and hands-on application, the code for these practical examples is made available through a code and data supplement and on GitHub.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324584","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

Stakeholders' Perspectives on Current Issues in Data Monitoring Committees 利益相关者对数据监测委员会当前问题的看法。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-09-22 DOI: 10.1002/bimj.202300384

Michael J. Cartwright, Tim Friede, David Lawrence, Emma May, Tobias Mütze, Kit Roes

{"title":"Stakeholders' Perspectives on Current Issues in Data Monitoring Committees","authors":"Michael J. Cartwright, Tim Friede, David Lawrence, Emma May, Tobias Mütze, Kit Roes","doi":"10.1002/bimj.202300384","DOIUrl":"10.1002/bimj.202300384","url":null,"abstract":"<div>\u0000 \u0000 Data Monitoring Committees (DMCs) are groups of experts that review accumulating data from one or more ongoing clinical studies and advise the Sponsor regarding the continuing safety of study subjects along with the continuing validity and scientific merit of the study. Although DMCs are widely used, considerable variability exists in their conduct. This paper offers recommendations, derived from sessions given at the 2023 Central European Network International Biometric and Statisticians in the Pharmaceutical Industry Conferences' and the authors' experiences. We focus on four topics that are part of the DMC process and where there is unclarity and inconsistency in current practices: (1) Communication with the DMC—We reflect on the importance of effective, proper communication channels between the DMC and relevant stakeholders to foster collaboration and exchange of critical information while retaining study integrity throughout. (2) Open sessions—We discuss the benefits of incorporating open sessions in DMC meetings to enhance transparency, inclusivity, and the consideration of diverse perspectives, as well as pitfalls of open sessions. (3) Access to efficacy data—We highlight the need for appropriate access to efficacy data by DMCs and discuss how to implement this in practice and how to address potential concerns regarding multiplicity. (4) Interactive data displays—We outline the utilization of interactive data displays to facilitate a more intuitive understanding of study results by the DMC. By addressing these topics, we aim to provide comprehensive practical recommendations that bridge the gap between current practices and optimal DMC functionality.\u0000 </div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301496","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

A Network-Constrain Weibull AFT Model for Biomarkers Discovery 用于生物标记物发现的网络应变 Weibull AFT 模型

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-09-22 DOI: 10.1002/bimj.202300272

Claudia Angelini, Daniela De Canditiis, Italia De Feis, Antonella Iuliano

引用次数: 0

Multivariate Scalar on Multidimensional Distribution Regression With Application to Modeling the Association Between Physical Activity and Cognitive Functions 多维分布的多变量标量回归应用于体育锻炼与认知功能之间关系的建模。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-09-22 DOI: 10.1002/bimj.202400042

Rahul Ghosal, Marcos Matabuena

{"title":"Multivariate Scalar on Multidimensional Distribution Regression With Application to Modeling the Association Between Physical Activity and Cognitive Functions","authors":"Rahul Ghosal, Marcos Matabuena","doi":"10.1002/bimj.202400042","DOIUrl":"10.1002/bimj.202400042","url":null,"abstract":"We develop a new method for multivariate scalar on multidimensional distribution regression. Traditional approaches typically analyze isolated univariate scalar outcomes or consider unidimensional distributional representations as predictors. However, these approaches are suboptimal because (i) they fail to utilize the dependence between the distributional predictors and (ii) neglect the correlation structure of the response. To overcome these limitations, we propose a multivariate distributional analysis framework that harnesses the power of multivariate density functions and multitask learning. We develop a computationally efficient semiparametric estimation method for modeling the effect of the latent joint density on the multivariate response of interest. Additionally, we introduce a new conformal prediction algorithm for quantifying the uncertainty of our multivariate predictions based on subject characteristics and individualized distributional predictors, providing valuable insights into the conditional distribution of the response. We validate the effectiveness of our proposed method through comprehensive numerical simulations, clearly demonstrating its superior performance compared to traditional methods. The application of the proposed method is demonstrated on triaxial accelerometer data from the National Health and Nutrition Examination Survey 2011–2014 for modeling the association between cognitive scores across various domains and distributional representation of physical activity among the older adult population. Our results highlight the advantages of the proposed approach, emphasizing the significance of incorporating multidimensional distributional information in the triaxial accelerometer data.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202400042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301495","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

Issue Information: Biometrical Journal 7'24 发行信息：生物计量学杂志 7'24

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-09-17 DOI: 10.1002/bimj.202470007

引用次数: 0

Investigating the Heterogeneity of “Study Twins” 调查 "研究双胞胎 "的异质性。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-09-02 DOI: 10.1002/bimj.202300387

Christian Röver, Tim Friede

引用次数: 0