Biometrical Journal最新文献_第2页

Two-Arm Crossover Randomized Controlled Trial Versus Meta-Analysis of N-of-1 Studies: Comparison of Statistical Efficiency in Determining an Intervention Effect 两组交叉随机对照试验与N-of-1研究的荟萃分析：确定干预效果的统计效率比较

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":"<p>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.</p>","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 带联系信息的排序集抽样生物特征函数的非参数估计

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 <p>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.</p>\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 基于堆叠模型的多组噪声标签分类器

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":"<p>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.</p>","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 高斯端点动态和特定参数信息借用的改进条件分段借用幂先验

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 <p>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.</p></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

Wavelet-Mixed Landmark Survival Models for the Effect of Short-Term Changes of Potassium in Heart Failure Patients 心衰患者钾短期变化影响的小波混合地标生存模型

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-03-06 DOI: 10.1002/bimj.70043

Caterina Gregorio, Giulia Barbati, Arjuna Scagnetto, Andrea di Lenarda, Francesca Ieva

{"title":"Wavelet-Mixed Landmark Survival Models for the Effect of Short-Term Changes of Potassium in Heart Failure Patients","authors":"Caterina Gregorio, Giulia Barbati, Arjuna Scagnetto, Andrea di Lenarda, Francesca Ieva","doi":"10.1002/bimj.70043","DOIUrl":"https://doi.org/10.1002/bimj.70043","url":null,"abstract":"<p>Statistical methods to study the association between a longitudinal biomarker and the risk of death are very relevant for the long-term care of subjects affected by chronic illnesses, such as potassium in heart failure patients. Particularly in the presence of comorbidities or pharmacological treatments, sudden crises can cause potassium to undergo very abrupt yet transient changes. In the context of the monitoring of potassium, there is a need for a dynamic model that can be used in clinical practice to assess the risk of death related to an observed patient's potassium trajectory. We considered different landmark survival approaches, starting from the simple approach considering the most recent measurement. We then propose a novel method based on wavelet filtering and landmarking to retrieve the prognostic role of past short-term potassium shifts. We argue that while taking into account the smooth changes in the biomarker, short-term changes cannot be overlooked. State-of-the-art dynamic survival models are prone to give more importance to the smooth component of the potassium profiles. However, our findings suggest that it is essential to also take into account recent potassium instability to capture all the relevant prognostic information. The data used comes from over 2000 subjects, with a total of over 80,000 repeated potassium measurements collected through administrative health records. The proposed wavelet landmark method revealed the prognostic role of past short-term changes in potassium. We also performed a simulation study to assess how and when to apply the proposed wavelet-mixed landmark model.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554576","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

Survivor Average Causal Effects for Continuous Time: A Principal Stratification Approach to Causal Inference With Semicompeting Risks 连续时间的幸存者平均因果效应：半竞争风险因果推理的主要分层方法

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-03-06 DOI: 10.1002/bimj.70041

Leah Comment, Fabrizia Mealli, Sebastien Haneuse, Corwin M. Zigler

{"title":"Survivor Average Causal Effects for Continuous Time: A Principal Stratification Approach to Causal Inference With Semicompeting Risks","authors":"Leah Comment, Fabrizia Mealli, Sebastien Haneuse, Corwin M. Zigler","doi":"10.1002/bimj.70041","DOIUrl":"https://doi.org/10.1002/bimj.70041","url":null,"abstract":"<div>\u0000 \u0000 <p>In semicompeting risks problems, nonterminal time-to-event outcomes, such as time to hospital readmission, are subject to truncation by death. These settings are often modeled with illness-death models for the hazards of the terminal and nonterminal events, but evaluating causal treatment effects with hazard models is problematic due to conditioning on survival—a posttreatment outcome—that is embedded in the definition of a hazard. Extending an existing survivor average causal effect (SACE) estimand, we frame the evaluation of treatment effects in the context of semicompeting risks with principal stratification and introduce two new causal estimands: the time-varying survivor average causal effect (TV-SACE) and the restricted mean survivor average causal effect (RM-SACE). These principal causal effects are defined among units that would survive regardless of assigned treatment. We adopt a Bayesian estimation procedure that parameterizes illness-death models for both treatment arms. We outline a frailty specification that can accommodate within-person correlation between nonterminal and terminal event times, and we discuss potential avenues for adding model flexibility. The method is demonstrated in the context of hospital readmission among late-stage pancreatic cancer patients.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554573","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

Issue Information: Biometrical Journal 2'25 期刊信息：biometic Journal 2'25

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-03-06 DOI: 10.1002/bimj.70049

引用次数: 0

Unscaled Indices for Assessing Agreement of Functional Data 评价功能数据一致性的非标度指标

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-02-18 DOI: 10.1002/bimj.70039

Kaeum Choi, Jeong Hoon Jang

{"title":"Unscaled Indices for Assessing Agreement of Functional Data","authors":"Kaeum Choi, Jeong Hoon Jang","doi":"10.1002/bimj.70039","DOIUrl":"https://doi.org/10.1002/bimj.70039","url":null,"abstract":"<div>\u0000 \u0000 <p>A decision to adopt a new medical device requires a rigorous assessment of the reliability and reproducibility of its clinical measurements. In this paper, with the goal of establishing the validity and acceptability of modern high-tech medical devices that generate functional data, we focus on the problem of assessing agreement of multiple functional data that are measured on the same subjects by different methods/technologies/raters. Specifically, we introduce a series of unscaled indices, total deviation index (TDI) and coverage probability (CP), that themselves are functions of time and can delineate the trends of intramethod, intermethod, and total (intra+inter) agreement of functional data across time in terms of the original measurement scale. We also develop scalar-valued TDI and CP indices that summarize the degree of agreement over the entire domain based on the weighted average idea. We advocate an experimental design under which each of the two methods generates replicated functional data measurements for each subject, and express each index using a mean function and variance components of a bivariate multilevel functional linear mixed effects model. Such a formulation allows us to smoothly estimate the indices based on our bivariate multilevel functional principal component analysis approach that only requires eigenanalyses of univariate covariance functions for better efficiency and scalability. Comprehensive simulation studies are conducted to examine the finite-sample properties of the estimators. The proposed method is applied to assess the reliability and reproducibility of renogram curves generated by diuresis renography, a high-tech medical imaging device widely used to detect kidney obstruction.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438780","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

High-Dimensional Variable Selection With Competing Events Using Cooperative Penalized Regression 基于合作惩罚回归的竞争事件高维变量选择

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-02-18 DOI: 10.1002/bimj.70036

Lukas Burk, Andreas Bender, Marvin N. Wright

{"title":"High-Dimensional Variable Selection With Competing Events Using Cooperative Penalized Regression","authors":"Lukas Burk, Andreas Bender, Marvin N. Wright","doi":"10.1002/bimj.70036","DOIUrl":"https://doi.org/10.1002/bimj.70036","url":null,"abstract":"<p>Variable selection is an important step in the analysis of high-dimensional data, yet there are limited options for survival outcomes in the presence of competing risks. Commonly employed penalized Cox regression considers each event type separately through cause-specific models, neglecting possibly shared information between them. We adapt the feature-weighted elastic net (fwelnet), an elastic net generalization, to survival outcomes and competing risks. For two causes, our proposed algorithm fits two alternating cause-specific models, where each model receives the coefficient vector of the complementary model as prior information. We dub this “cooperative penalized regression,” as it enables the modeling of competing risk data with cause-specific models while accounting for shared effects between causes. Coefficients that are shrunken toward zero in the model for the first cause will receive larger penalization weights in the model for the second cause and vice versa. Through multiple iterations, this process ensures stronger penalization of uninformative predictors in both models. We demonstrate our method's variable selection capabilities on simulated genomics data and apply it to bladder cancer microarray data. We evaluate selection performance using the positive predictive value for the correct selection of informative features and the false positive rate for the selection of uninformative variables. The benchmark compares results with cause-specific penalized Cox regression, random survival forests, and likelihood-boosted Cox regression. Results indicate that our approach is more effective at selecting informative features and removing uninformative features. In settings without shared effects, variable selection performance is similar to cause-specific penalized Cox regression.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438781","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

Parametric Estimation of the Mean Number of Events in the Presence of Competing Risks 存在竞争风险时平均事件数的参数估计

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-02-18 DOI: 10.1002/bimj.70038

Joshua P. Entrop, Lasse H. Jakobsen, Michael J. Crowther, Mark Clements, Sandra Eloranta, Caroline E. Dietrich

{"title":"Parametric Estimation of the Mean Number of Events in the Presence of Competing Risks","authors":"Joshua P. Entrop, Lasse H. Jakobsen, Michael J. Crowther, Mark Clements, Sandra Eloranta, Caroline E. Dietrich","doi":"10.1002/bimj.70038","DOIUrl":"https://doi.org/10.1002/bimj.70038","url":null,"abstract":"<p>Recurrent events, for example, hospitalizations or drug prescriptions, are common in time-to-event research. One useful summary measure of the recurrent event process is the mean number of events. Methods for estimating the mean number of events exist and are readily implemented for situations in which the recurrent event is the only possible outcome. However, estimation gets more challenging in the competing risk setting, in which methods are so far limited to nonparametric approaches. To this end, we propose a postestimation command for estimating the mean number of events in the presence of competing risks by jointly modeling the intensity function of the recurrent event and the survival function for the competing events. The proposed method is implemented in the R-package <span>JointFPM</span> which is available on CRAN. Simulations demonstrate low bias and good coverage in scenarios where the intensity of the recurrent event does not depend on the number of previous events. We illustrate our method using data on readmissions after colorectal cancer surgery included in the <span>frailtypack</span> package for R. Estimates of the mean number of events can be used to augment time-to-event analyses when both recurrent and competing events exist. The proposed parametric approach offers estimation of a smooth function across time as well as easy estimation of different contrasts which is not available using a nonparametric approach.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438779","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