Biometrical Journal最新文献

Choosing an Appropriate Outlier-Detection Method When Comparing Institutional Performances in Clinical Audits 在临床审计机构绩效比较中选择合适的异常值检测方法

IF 2.1 3区生物学

Biometrical Journal Pub Date : 2026-10-22 DOI: 10.1002/bimj.70196

Anqi Sui, Gareth Ambler, Rumana Z. Omar, Mark de Belder, Menelaos Pavlou

{"title":"Choosing an Appropriate Outlier-Detection Method When Comparing Institutional Performances in Clinical Audits","authors":"Anqi Sui, Gareth Ambler, Rumana Z. Omar, Mark de Belder, Menelaos Pavlou","doi":"10.1002/bimj.70196","DOIUrl":"https://doi.org/10.1002/bimj.70196","url":null,"abstract":"Clinical audits are essential for monitoring and improving the quality of healthcare with statistical outlier detection used to identify healthcare units whose performance deviates from expected standards. Two commonly used outlier-detection approaches are Common Mean Model (CMM) and Random Effects Logistic Regression (RELR). However, these methods can identify different healthcare units as outliers when applied to the same dataset. Incorrectly flagging a unit as an outlier can have serious consequences, including reputational harm, decreased staff morale, unnecessary regulatory scrutiny, diversion of resources, and loss of public trust. This article investigates the reasons for these discrepancies through a simulation study evaluating the performance of the CMM and RELR under a range of scenarios. The results show that differences in outlier detection are primarily driven by the degree to which a normality assumption holds for each method. When these assumptions are violated, for one or both methods, the two approaches may produce markedly different outlier detection. To choose the most appropriate approach for a given dataset, we recommend assessing each normality assumption, by combining visual diagnostics, such as QQ plots, with quantitative measures, such as Lin's concordance correlation coefficient (CCC).","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"68 6","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70196","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149235454","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

Assessing Multitreatment Effects for Discrete Observational Data 评估离散观测数据的多重处理效应

IF 2.1 3区生物学

Biometrical Journal Pub Date : 2026-10-22 DOI: 10.1002/bimj.70194

Cugnata Federica, Conti Pier Luigi, Vicard Paola, Briganti Alberto, Mecatti Fulvia, Di Serio Clelia, Rancoita Paola Maria Vittoria

引用次数: 0

Issue Information: Biometrical Journal 6'26 期刊信息：biometic Journal 6'26

IF 2.1 3区生物学

Biometrical Journal Pub Date : 2026-10-22 DOI: 10.1002/bimj.70198

引用次数: 0

Estimands Before Algorithms: Opioid Agonist Treatment Retention as a Competing-Risk Problem 算法之前的估计：阿片类激动剂治疗保留作为一个竞争风险问题

IF 2.1 3区生物学

Biometrical Journal Pub Date : 2026-10-22 DOI: 10.1002/bimj.70190

Chia-Jo Chuang, Lien-Chung Wei

引用次数: 0

Reply to “Estimands Before Algorithms: Opioid Agonist Treatment Retention as a Competing-Risk Problem” 回复“算法之前的估计：阿片类激动剂治疗保留作为一个竞争风险问题”

IF 2.1 3区生物学

Biometrical Journal Pub Date : 2026-10-22 DOI: 10.1002/bimj.70191

Christoph Wiederkehr, Christian Heumann, Michael Schomaker

引用次数: 0

Optimal Designs for Identifying Effective Doses in Drug Combination Studies. 联合用药研究中确定有效剂量的最佳设计。

IF 2.1 3区生物学

Biometrical Journal Pub Date : 2026-10-01 DOI: 10.1002/bimj.70186

Leonie Schürmeyer, Ludger Sandig, Jorrit Kühne, Leonie T Hezler, Bernd-Wolfgang Igl, Kirsten Schorning

{"title":"Optimal Designs for Identifying Effective Doses in Drug Combination Studies.","authors":"Leonie Schürmeyer, Ludger Sandig, Jorrit Kühne, Leonie T Hezler, Bernd-Wolfgang Igl, Kirsten Schorning","doi":"10.1002/bimj.70186","DOIUrl":"10.1002/bimj.70186","url":null,"abstract":"We consider the optimal design problem for identifying effective dose combinations within drug combination studies where the effect of the combination of two drugs is investigated. Drug combination studies are becoming increasingly important as they investigate potential interaction effects rather than the individual impacts of the drugs. In this situation, identifying effective dose combinations that yield a prespecified effect is of special interest. If nonlinear surface models are used to describe the dose combination-response relationship, these effective dose combinations, also called isoboles, result in specific contour lines of the fitted response model. Consequently, accurately estimating these effective dose combinations is crucial. We propose a novel design criterion that targets the precise estimation of these effective dose combinations. In particular, an optimal design minimizes the width of the confidence band of the contour lines of interest. Optimal design theory is developed for this problem, including equivalence theorems and efficiency bounds. The performance of the optimal design is illustrated in different examples modeling dose combination data by various nonlinear surface models. It is demonstrated that the proposed optimal design for identifying effective dose combinations yields a more precise estimation of the effective dose combinations than ray or factorial designs, which are commonly used in practice. This particularly holds true for a case study motivated by data from an oncological dose combination study.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"68 5","pages":"e70186"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13620475/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076838","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 Reversed Multi-Arm Two-Stage (RevMATS) Design in Oncology Dose Optimization. 肿瘤剂量优化的逆向多臂两阶段设计

IF 2.1 3区生物学

Biometrical Journal Pub Date : 2026-10-01 DOI: 10.1002/bimj.70195

Yue Yang, Gu Mi, Ji Lin

{"title":"The Reversed Multi-Arm Two-Stage (RevMATS) Design in Oncology Dose Optimization.","authors":"Yue Yang, Gu Mi, Ji Lin","doi":"10.1002/bimj.70195","DOIUrl":"https://doi.org/10.1002/bimj.70195","url":null,"abstract":"The multi-arm two-stage (MATS) design combines proof-of-concept and dose optimization in a single study, which starts from parallel enrollments of participants across multiple cohorts (indications) at a dose selected from the dose-escalation study, which is usually the maximum tolerable dose (MTD) in traditional oncology dose-escalation studies, followed by randomizing subjects into two dose levels only for those \"efficaciously promising\" cohorts at the second stage. Here, we communicate a variant of the MATS design by \"reversing\" the two stages so that randomization to two dose levels for each indication-specific cohort precedes potential further enrollment at the dose selected for optimal efficacy/safety profile based on the randomization stage. This \"Reversed MATS\" (RevMATS) design fits into certain situations when prior data evidence indicates two doses may result in similar activities or there is not enough evidence to favor one dose over the other in terms of preliminary activities, thus justifying earlier randomization of two doses instead of delaying to the second stage. It also fits when one dose has revealed promising early efficacy signals in a heterogenous population and there is reasonable belief of effectiveness across indications (so that a larger upfront investment is justified so that the second stage of MATS may be skipped). The RevMATS design further improves the underlying Bayesian hierarchical model in the MATS design by accounting for broader (potential) efficacy profiles of candidate doses and explicitly incorporating collective data evidence, such as estimated dose-response curves (from dose escalation), to boost efficiency in decision-making.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"68 5","pages":"e70195"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186396","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

Confidence Intervals for a Proportion Estimated From Pooled Samples Based on Firth's Corrected Score. 基于Firth校正分数的汇总样本估计比例的置信区间。

IF 2.1 3区生物学

Biometrical Journal Pub Date : 2026-10-01 DOI: 10.1002/bimj.70185

Brad J Biggerstaff, Graham Hepworth

{"title":"Confidence Intervals for a Proportion Estimated From Pooled Samples Based on Firth's Corrected Score.","authors":"Brad J Biggerstaff, Graham Hepworth","doi":"10.1002/bimj.70185","DOIUrl":"10.1002/bimj.70185","url":null,"abstract":"Interval estimation of a proportion estimated from testing of pooled samples has been studied by a variety of researchers, who have examined both exact and asymptotic methods. Recent work in point estimation has seen the use of Firth's correction to maximum likelihood estimation to reduce bias effectively. We consider the Firth-corrected score statistic, using either expected or observed information, as the first derivative function of a penalized likelihood and develop score-based confidence intervals wholly within this framework. We evaluate their performance, comparing them by information type and to the existing, recommended asymptotic method of Gart. The methods are illustrated using data on West Nile virus infection in field-collected mosquitoes and on yellow fever virus infection in mosquitoes reported in Walter et al. The penalized likelihood approach using expected information is found to unify previously recommended point and interval estimates under one inferential framework. Detailed evaluation of coverage properties of the intervals over a range of pooling designs show improved performance for the intervals using observed information and skewness-correction to the score. We derive simple, interpretable expressions for the score and skewness-corrected score statistics, and give a quadratic formula for the skewness-corrected score interval for the case that all pools are the same size, which reduces to a skewness-correction for the standard Wilson interval in the standard binomial case.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"68 5","pages":"e70185"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13620447/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076808","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

Bayesian Hierarchical Modeling Approaches for Combining Information From Multiple Data Sources to Produce Annual Estimates of National Immunization Coverage. 结合多个数据源信息的贝叶斯分层建模方法以产生国家免疫覆盖率的年度估计。

IF 2.1 3区生物学

Biometrical Journal Pub Date : 2026-10-01 DOI: 10.1002/bimj.70189

C Edson Utazi, Warren C Jochem, M Carolina Danovaro-Holliday, Padraic Murphy, Sujit K Sahu, Marta Gacic-Dobo, Andrew J Tatem

{"title":"Bayesian Hierarchical Modeling Approaches for Combining Information From Multiple Data Sources to Produce Annual Estimates of National Immunization Coverage.","authors":"C Edson Utazi, Warren C Jochem, M Carolina Danovaro-Holliday, Padraic Murphy, Sujit K Sahu, Marta Gacic-Dobo, Andrew J Tatem","doi":"10.1002/bimj.70189","DOIUrl":"10.1002/bimj.70189","url":null,"abstract":"Estimates of national immunization coverage (ENIC) are crucial for guiding policy and decision-making in national immunization programs and setting the global immunization agenda. World Health Organization (WHO) and UNICEF estimates of national immunization coverage (WUENIC) are produced annually for various vaccine-dose combinations and all WHO Member States using information from multiple data sources and a deterministic computational logic approach. This approach, however, is incapable of characterizing the uncertainties inherent in coverage measurement and estimation. It also provides no statistically principled way of exploiting and accounting for the interdependence in immunization coverage data collected for multiple vaccines, countries, and time points. Here, we develop Bayesian hierarchical modeling approaches for producing accurate ENIC and associated uncertainties. We propose and explore two candidate models: a base single likelihood (BSL) model and an irregular data multiple likelihood (IDML) model, both of which differ in their handling of missing data and characterization of the uncertainties associated with the multiple input data sources. We provide a simulation study that demonstrates a high degree of accuracy of the estimates produced by the proposed models, and which also shows that the IDML model generally outperformed the BSL model in most cases we considered. We apply the methodology to produce coverage estimates for select vaccine-dose combinations for the period 2000-2019. A contributed R package, imcover, implementing the No-U-Turn Sampler in the Stan programming language enhances the utility and reproducibility of the methodology.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"68 5","pages":"e70189"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13652340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149118733","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

Multivariate Analysis of Data From Crossover Clinical Trials. 交叉临床试验数据的多变量分析。

IF 2.1 3区生物学

Biometrical Journal Pub Date : 2026-10-01 DOI: 10.1002/bimj.70193

Yi Jiang, Vernon M Chinchilli

{"title":"Multivariate Analysis of Data From Crossover Clinical Trials.","authors":"Yi Jiang, Vernon M Chinchilli","doi":"10.1002/bimj.70193","DOIUrl":"10.1002/bimj.70193","url":null,"abstract":"In crossover designs, multiple outcomes are often collected to assess treatment effectiveness in a comprehensive manner. Also, interest has grown in comparing intra-subject variability between treatments in such designs. We present three distinct multivariate linear mixed-effects models, with the choice of the model depending on the design and assumptions regarding carryover effects. We discuss maximum likelihood (ML) and restricted ML (REML) approaches for parameter estimation. For a special case that a crossover design is uniform within sequences, we derive the closed-form expressions for the ML and REML estimators of the variance components. We propose general hypothesis testing methods for the treatment effects and the intra-subject variance matrices. The latter situation has not received much attention in the statistical literature. For the special case, we derive the exact test statistics for treatment means comparisons and for testing the null hypothesis of homogeneous intra-subject variance matrices. Simulation studies are conducted to validate the superior performance of the proposed multivariate approach compared with the usual univariate approach in testing treatment effects. Finally, we apply the methodologies to two data examples.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"68 5","pages":"e70193"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13655226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149171217","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