Biometrical Journal最新文献

How Should Parallel Cluster Randomized Trials With a Baseline Period be Analyzed?—A Survey of Estimands and Common Estimators 有基线期的平行群随机试验应该如何分析？-估价及一般估价员概览

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-04-29 DOI: 10.1002/bimj.70052

Kenneth Menglin Lee, Fan Li

{"title":"How Should Parallel Cluster Randomized Trials With a Baseline Period be Analyzed?—A Survey of Estimands and Common Estimators","authors":"Kenneth Menglin Lee, Fan Li","doi":"10.1002/bimj.70052","DOIUrl":"https://doi.org/10.1002/bimj.70052","url":null,"abstract":"The parallel cluster randomized trial with baseline (PB-CRT) is a common variant of the standard parallel cluster randomized trial (P-CRT). We define two natural estimands in the context of PB-CRTs with informative cluster sizes, the individual-average treatment effect (iATE) and cluster-average treatment effect (cATE), to address individual and cluster-level hypotheses. In this work, we theoretically derive the convergence of the unweighted and inverse cluster-period size weighted (i) independence estimating equation (IEE), (ii) fixed-effects (FE) model, (iii) exchangeable mixed-effects (EME) model, and (iv) nested-exchangeable mixed-effects (NEME) model treatment effect estimators in a PB-CRT with informative cluster sizes and continuous outcomes. Overall, we theoretically show that the unweighted and weighted IEE and FE models yield consistent estimators for the iATE and cATE estimands. Although mixed-effects models yield inconsistent estimators to these two natural estimands under informative cluster sizes, we empirically demonstrate that the EME model is surprisingly robust to bias. This is in sharp contrast to the corresponding analyses in P-CRTs and the NEME model in PB-CRTs when informative cluster sizes are present, carrying implications for practice. We report a simulation study and conclude with a re-analysis of a PB-CRT examining the effects of community youth teams on improving mental health among adolescent girls in rural eastern India.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889149","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

A Regularized MANOVA Test for Semicontinuous High-Dimensional Data 半连续高维数据的正则化方差检验

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-04-29 DOI: 10.1002/bimj.70054

Elena Sabbioni, Claudio Agostinelli, Alessio Farcomeni

引用次数: 0

Bayesian Inference of Phenotypic Plasticity of Cancer Cells Based on Dynamic Model for Temporal Cell Proportion Data 基于时间细胞比例数据动态模型的癌细胞表型可塑性贝叶斯推断

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-04-29 DOI: 10.1002/bimj.70055

Shuli Chen, Yuman Wang, Da Zhou, Jie Hu

{"title":"Bayesian Inference of Phenotypic Plasticity of Cancer Cells Based on Dynamic Model for Temporal Cell Proportion Data","authors":"Shuli Chen, Yuman Wang,  Da Zhou, Jie Hu","doi":"10.1002/bimj.70055","DOIUrl":"https://doi.org/10.1002/bimj.70055","url":null,"abstract":"<div>\u0000 \u0000 Mounting evidence underscores the prevalent hierarchical organization of cancer tissues. At the foundation of this hierarchy reside cancer stem cells, a subset of cells endowed with the pivotal role of engendering the entire cancer tissue through cell differentiation. In recent times, substantial attention has been directed toward the phenomenon of cancer cell plasticity, where the dynamic interconversion between cancer stem cells and nonstem cancer cells has garnered significant interest. Since the task of detecting cancer cell plasticity from empirical data remains a formidable challenge, we propose a Bayesian statistical framework designed to infer phenotypic plasticity within cancer cells, utilizing temporal data on cancer stem cell proportions. Our approach is grounded in a stochastic model, adept at capturing the dynamic behaviors of cells. Leveraging Bayesian analysis, we scrutinize the moment equation governing cancer stem cell proportions, derived from the Kolmogorov forward equation of our stochastic model. Our methodology introduces an improved Euler method for parameter estimation within nonlinear ordinary differential equation models, also extending insights to compositional data. Extensive simulations robustly validate the efficacy of our proposed method. To further corroborate our findings, we apply our approach to analyze published data from SW620 colon cancer cell lines. Our results harmonize with in situ experiments, thereby reinforcing the utility of our method in discerning and quantifying phenotypic plasticity within cancer cells.</div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884019","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

Impact of Methodological Assumptions and Covariates on the Cutoff Estimation in ROC Analysis 方法假设和协变量对ROC分析中截止估计的影响

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-04-27 DOI: 10.1002/bimj.70053

Soutik Ghosal

{"title":"Impact of Methodological Assumptions and Covariates on the Cutoff Estimation in ROC Analysis","authors":"Soutik Ghosal","doi":"10.1002/bimj.70053","DOIUrl":"https://doi.org/10.1002/bimj.70053","url":null,"abstract":"The receiver operating characteristic (ROC) curve stands as a cornerstone in assessing the efficacy of biomarkers for disease diagnosis. Beyond merely evaluating performance, it provides with an optimal cutoff for biomarker values, crucial for disease categorization. While diverse methodologies exist for cutoff estimation, less attention has been paid to integrating covariate impact into this process. Covariates can strongly impact diagnostic summaries, leading to variations across different covariate levels. Therefore, a tailored covariate-based framework is imperative for outlining covariate-specific optimal cutoffs. Moreover, recent investigations into cutoff estimators have overlooked the influence of ROC curve estimation methodologies. This study endeavors to bridge this gap by addressing the research void. Extensive simulation studies are conducted to scrutinize the performance of ROC curve estimation models in estimating different cutoffs in varying scenarios, encompassing diverse data-generating mechanisms and covariate effects. In addition, leveraging the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set, the research assesses the performance of different biomarkers in diagnosing Alzheimer's disease and determines the suitable optimal cutoffs.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879963","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

On Sample Size Determination for Augmented Tests Based on Restricted Mean Survival Time in Randomized Clinical Trials 随机临床试验中基于限制平均生存时间的增强试验的样本量确定

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-03-24 DOI: 10.1002/bimj.70046

Satoshi Hattori, Hajime Uno

{"title":"On Sample Size Determination for Augmented Tests Based on Restricted Mean Survival Time in Randomized Clinical Trials","authors":"Satoshi Hattori, Hajime Uno","doi":"10.1002/bimj.70046","DOIUrl":"https://doi.org/10.1002/bimj.70046","url":null,"abstract":"Restricted mean survival time (RMST) is gaining attention as a measure to quantify the treatment effect on survival outcomes in randomized clinical trials. Several methods to determine sample size based on the RMST-based tests have been proposed. However, to the best of our knowledge, there is no discussion about the power and sample size regarding the augmented version of RMST-based tests, which utilize baseline covariates for a gain in estimation efficiency and in power for testing no treatment effect. The conventional event-driven study design based on the logrank test allows us to calculate the power for a given hazard ratio without specifying the survival functions. In contrast, the existing sample size determination methods for the RMST-based tests relies on the adequacy of the assumptions of the entire survival curves of two groups. Furthermore, to handle the augmented test, the correlation between the baseline covariates and the martingale residuals must be handled. To address these issues, we propose an approximated sample size formula for the augmented version of the RMST-based test, which does not require specifying the entire survival curve in the treatment group, and also a sample size recalculation approach to update the correlations between the baseline covariates and the martingale residuals with the blinded data. The proposed procedure will enable the studies to have the target power for a given RMST difference even when correct survival functions cannot be specified at the design stage.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689854","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

Editorial for the Special Collection “MCP 2022” 《MCP 2022》特辑社论

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-03-24 DOI: 10.1002/bimj.70047

Werner Brannath, Thorsten Dickhaus, Ruth Heller, Jesse Hemerik

{"title":"Editorial for the Special Collection “MCP 2022”","authors":"Werner Brannath, Thorsten Dickhaus, Ruth Heller, Jesse Hemerik","doi":"10.1002/bimj.70047","DOIUrl":"https://doi.org/10.1002/bimj.70047","url":null,"abstract":"This special collection on Multiple Comparisons arose from the 12th International Conference on Multiple Comparison Procedures (MCP 2022) that took place from August 30 to September 2, 2022, at the University of Bremen, Germany. The conference was hosted locally by Professors Werner Brannath and Thorsten Dickhaus. MCP 2022 continued the tradition of this conference series. The contributions to the conference covered the latest methodological and applied developments in the areas of simultaneous and selective inference, including testing, confidence intervals, estimation, adaptive designs, statistical modelling, and machine learning approaches, under a variety of error rates to be controlled.This article collection contains theoretical papers on multiple comparisons by Budig et al. (2024), Chen et al. (2024), Pöhlmann et al. (2024), and by Ochieng et al. (2024). Several sessions of MCP 2022 included contributions dealing with online control of the family-wise error rate or the false discovery rate, respectively. The papers by Fischer et al. (2024) and by Fisher (2024) in this special collection reflect this current research direction. Bounding the number or the proportion, respectively, of false discoveries is considered in the papers by Xu et al. (2024) and by Zheng et al. (2024). Statistical methods for planning and evaluating studies with adaptive or group-sequential designs are developed in the papers by Danzer et al. (2024) and by Zhao et al. (2025), and platform trials are studied by Greenstreet et al. (2025).After the long time without face-to-face meetings because of the COVID-19 pandemic, the conference delegates (Figure 1) enjoyed the social program of MCP 2022, which included an evening reception in Bremen's historic Town Hall as well as a boat trip to Bremerhaven.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689720","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

The Shared Weighted Lindley Frailty Model for Clustered Failure Time Data 聚类故障时间数据的共享加权Lindley脆弱性模型

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-03-19 DOI: 10.1002/bimj.70044

Diego I. Gallardo, Marcelo Bourguignon, John L. Santibáñez

引用次数: 0

A New Frequentist Implementation of the Daniels and Hughes Bivariate Meta-Analysis Model for Surrogate Endpoint Evaluation 丹尼尔斯和休斯双变量元分析模型的新频率实现替代终点评估

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-03-19 DOI: 10.1002/bimj.70048

Dan Jackson, Michael Sweeting, Robbie C. M. van Aert, Sylwia Bujkiewicz, Keith R. Abrams, Wolfgang Viechtbauer

{"title":"A New Frequentist Implementation of the Daniels and Hughes Bivariate Meta-Analysis Model for Surrogate Endpoint Evaluation","authors":"Dan Jackson, Michael Sweeting, Robbie C. M. van Aert, Sylwia Bujkiewicz, Keith R. Abrams, Wolfgang Viechtbauer","doi":"10.1002/bimj.70048","DOIUrl":"https://doi.org/10.1002/bimj.70048","url":null,"abstract":"Surrogate endpoints are used when the primary outcome is difficult to measure accurately. Determining if a measure is suitable to use as a surrogate endpoint is a challenging task and a variety of meta-analysis models have been proposed for this purpose. The Daniels and Hughes bivariate model for trial-level surrogate endpoint evaluation is gaining traction but presents difficulties for frequentist estimation and hitherto only Bayesian solutions have been available. This is because the marginal model is not a conventional linear model and the number of unknown parameters increases at the same rate as the number of studies. This second property raises immediate concerns that the maximum likelihood estimator of the model's unknown variance component may be downwardly biased. We derive maximum likelihood estimating equations to motivate a bias adjusted estimator of this parameter. The bias correction terms in our proposed estimating equation are easily computed and have an intuitively appealing algebraic form. A simulation study is performed to illustrate how this estimator overcomes the difficulties associated with maximum likelihood estimation. We illustrate our methods using two contrasting examples from oncology.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646005","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

Spatially Informed Nonnegative Matrix Trifactorization for Coclustering Mass Spectrometry Data 聚类质谱数据的空间通知非负矩阵三因子分解

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-03-19 DOI: 10.1002/bimj.70031

Andrea Sottosanti, Francesco Denti, Stefania Galimberti, Davide Risso, Giulia Capitoli

{"title":"Spatially Informed Nonnegative Matrix Trifactorization for Coclustering Mass Spectrometry Data","authors":"Andrea Sottosanti, Francesco Denti, Stefania Galimberti, Davide Risso, Giulia Capitoli","doi":"10.1002/bimj.70031","DOIUrl":"https://doi.org/10.1002/bimj.70031","url":null,"abstract":"Mass spectrometry imaging techniques measure molecular abundance in a tissue sample at a cellular resolution, all while preserving the spatial structure of the tissue. This kind of technology offers a detailed understanding of the role of several molecular factors in biological systems. For this reason, the development of fast and efficient computational methods that can extract relevant signals from massive experiments has become necessary. A key goal in mass spectrometry data analysis is the identification of molecules with similar functions in the analyzed biological system. This result can be achieved by studying the spatial distribution of the molecules' abundance patterns. To do so, one can perform coclustering, that is, dividing the molecules into groups according to their expression patterns over the tissue and segmenting the tissue according to the molecules' abundance levels. We present TRIFASE, a semi-nonnegative matrix trifactorization technique that performs coclustering while accounting for the spatial correlation of the data. We propose an estimation algorithm that solves the proposed matrix trifactorization problem. Moreover, to improve scalability, we also propose two heuristic approximations of the most expensive steps, which help the algorithm converge while significantly streamlining the computational cost. We validated our method on a series of simulation experiments, comparing the different estimating strategies discussed in the article. Last, we analyzed a mouse brain tissue sample processed with MALDI-MSI technology, showing how TRIFASE extracts specific expression patterns of molecule abundance in localized tissue areas and discovers blocks of proteins whose activation is directly linked to specific biological mechanisms.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646003","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

Toward Power Analysis for Partial Least Squares-Based Methods 基于偏最小二乘方法的功率分析研究

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-03-13 DOI: 10.1002/bimj.70050

Angela Andreella, Livio Finos, Bruno Scarpa, Matteo Stocchero

引用次数: 0