Biometrical Journal最新文献

On Sample Size Determination for Augmented Tests Based on Restricted Mean Survival Time in Randomized Clinical Trials

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”

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

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

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

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

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

Two-Arm Crossover Randomized Controlled Trial Versus Meta-Analysis of N-of-1 Studies: Comparison of Statistical Efficiency in Determining an Intervention Effect

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-03-12 DOI: 10.1002/bimj.70045

Anna Eleonora Carrozzo, Georg Zimmermann, Arne C. Bathke, Daniel Neunhaeuserer, Josef Niebauer, Stefan T. Kulnik

{"title":"Two-Arm Crossover Randomized Controlled Trial Versus Meta-Analysis of N-of-1 Studies: Comparison of Statistical Efficiency in Determining an Intervention Effect","authors":"Anna Eleonora Carrozzo, Georg Zimmermann, Arne C. Bathke, Daniel Neunhaeuserer, Josef Niebauer, Stefan T. Kulnik","doi":"10.1002/bimj.70045","DOIUrl":"https://doi.org/10.1002/bimj.70045","url":null,"abstract":"N-of-1 trials are currently receiving broader attention in healthcare research when assessing the effectiveness of interventions. In contrast to the most commonly applied two-arm randomized controlled trial (RCT), in an N-of-1 design, the individual acts as their own control condition in the sense of a multiple crossover trial. N-of-1 trials can lead to a higher quality of patient by examining the effectiveness of an intervention at an individual level. Moreover, when a series of N-of-1 trials are properly aggregated, it becomes possible to detect an intervention effect at a population level. This work investigates whether a meta-analysis of summary data of a series of N-of-1 trials allows us to detect a statistically significant intervention effect with fewer participants than in a traditional, prospectively powered two-arm RCT and crossover design when evaluating a digital health intervention in cardiovascular care. After introducing these different analysis approaches, we compared the empirical properties in a simulation study both under the null hypothesis and with respect to power with different between-subject heterogeneity settings and in the presence of a carry-over effect. We further investigate the performance of a sequential aggregation procedure. In terms of simulated power, the threshold of 80% was achieved earlier for the aggregating procedure, requiring fewer participants.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602578","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

Nonparametric Estimation of a Biometric Function Using Ranked Set Sampling With Ties Information

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-03-12 DOI: 10.1002/bimj.70007

Leila Jabari Koopaei, Ehsan Zamanzade, Afshin Parvardeh, Xinlei Wang

{"title":"Nonparametric Estimation of a Biometric Function Using Ranked Set Sampling With Ties Information","authors":"Leila Jabari Koopaei, Ehsan Zamanzade, Afshin Parvardeh, Xinlei Wang","doi":"10.1002/bimj.70007","DOIUrl":"https://doi.org/10.1002/bimj.70007","url":null,"abstract":"<div>\u0000 \u0000 The mean residual life (MRL) function plays an important role in the summary and analysis of survival data. The main advantage of this function is that it summarizes the information in units of time instead of a probability scale, which requires careful interpretation. Ranked set sampling (RSS) is a sampling technique designed for situations, where obtaining precise measurements of sample units is expensive or difficult, but ranking them without referring to their accurate values is cost-effective or easy. However, the practical application of RSS is hindered because each sample unit is required to assign a unique rank. To alleviate this difficulty, Frey developed a novel variation of RSS, called RSS-t, that records and utilizes the tie structure in the ranking process. In this paper, we propose several different nonparametric estimators for the MRL function based on RSS-t. Then, we compare the proposed estimators with their counterparts in simple random sampling (SRS) and RSS, where tie information is not utilized. We also implemented our proposed estimators on a real data set related to patient waiting times for liver transplantation, to show their applicability and efficiency in practice. Our results show that using ties information leads to an improved statistical inference for the MRL function, and therefore a smaller sample size is needed to reach a predetermined precision.\u0000 </div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602607","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

Stacking Model-Based Classifiers for Dealing With Multiple Sets of Noisy Labels

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-03-12 DOI: 10.1002/bimj.70042

Giulia Montani, Andrea Cappozzo

{"title":"Stacking Model-Based Classifiers for Dealing With Multiple Sets of Noisy Labels","authors":"Giulia Montani, Andrea Cappozzo","doi":"10.1002/bimj.70042","DOIUrl":"https://doi.org/10.1002/bimj.70042","url":null,"abstract":"Supervised learning in presence of multiple sets of noisy labels is a challenging task that is receiving increasing interest in the ever-evolving landscape of healthcare analytics. Such an issue arises when multiple annotators are tasked to manually label the same training samples, potentially giving rise to discrepancies in class assignments among the supplied labels with respect to the ground truth. Commonly, the labeling process is entrusted to a small group of domain experts, and different level of experience and subjectivity may result in noisy training labels. To solve the classification task leveraging on the availability of multiple data annotators, we introduce a novel ensemble methodology constructed combining model-based classifiers separately trained on single sets of noisy labels. Eigenvalue Decomposition Discriminant Analysis is employed for the definition of the base learners, and six distinct averaging strategies are proposed to combine them. Two solutions necessitate a priori information, such as the partial knowledge of the ground truth labels or the annotators' level of expertise. Differently, the remaining four approaches are entirely data-driven. A simulation study and an application on real data showcase the improved predictive performance of our proposal, while also demonstrating the ability of automatically inferring annotators' expertise level as a by-product of the learning process.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602577","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

Modified Conditional Borrowing-By-Part Power Prior for Dynamic and Parameter-Specific Information Borrowing of the Gaussian Endpoint

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-03-12 DOI: 10.1002/bimj.70029

Kai Wang, Han Cao, Chen Yao

{"title":"Modified Conditional Borrowing-By-Part Power Prior for Dynamic and Parameter-Specific Information Borrowing of the Gaussian Endpoint","authors":"Kai Wang, Han Cao, Chen Yao","doi":"10.1002/bimj.70029","DOIUrl":"https://doi.org/10.1002/bimj.70029","url":null,"abstract":"<div>\u0000 \u0000 Borrowing external controls to augment the concurrent control arm is a popular topic in clinical trials. Bayesian dynamic borrowing methods adaptively discount external controls according to prior-data conflict. For the Gaussian endpoint, parameter-specific information borrowing enables differential discounting between the population mean and variance. The borrowing-by-part power prior employs two power parameters to separately downweight external likelihoods concerning the sample mean and variance. However, within the fully Bayesian framework, the posterior inference of the average treatment effect (ATE) defined as the population mean difference is significantly affected by the variance-specific prior-data conflict that reflects the heterogeneity of population variance. Here, we propose the modified conditional borrowing-by-part power prior (MCBPP) that separately discounts the external sample mean and variance according to parameter-specific prior-data conflicts, resulting in a more stable posterior estimation of ATE than its competitors under the same degree of mean-specific prior-data conflict. By fully discounting the external sample variance, the robust MCBPP (rMCBPP) can yield robust posterior inference of ATE against the variance-specific prior-data conflict. Although the population variance is considered a nuisance parameter, its homogeneity is equally important to justify information borrowing. We recommend the rMCBPP for borrowing external controls with a similar sample variance to concurrent controls because it exhibits better control of bias and Type I error rate than the modified power prior (MPP) assuming unknown variance in the absence of population variance heterogeneity. However, when faced with a significant sample variance discrepancy, the MPP assuming unknown variance is preferred given its better performance under severe population variance heterogeneity.</div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602576","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