Biometrical Journal最新文献_第2页

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

Network Meta-Analysis of Time-to-Event Endpoints With Individual Participant Data Using Restricted Mean Survival Time Regression 使用限制平均生存时间回归的个体参与者数据的时间到事件终点的网络元分析

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-02-18 DOI: 10.1002/bimj.70037

Kaiyuan Hua, Xiaofei Wang, Hwanhee Hong

{"title":"Network Meta-Analysis of Time-to-Event Endpoints With Individual Participant Data Using Restricted Mean Survival Time Regression","authors":"Kaiyuan Hua, Xiaofei Wang, Hwanhee Hong","doi":"10.1002/bimj.70037","DOIUrl":"https://doi.org/10.1002/bimj.70037","url":null,"abstract":"<div>\u0000 \u0000 <p>Network meta-analysis (NMA) extends pairwise meta-analysis to compare multiple treatments simultaneously by combining “direct” and “indirect” comparisons of treatments. The availability of individual participant data (IPD) makes it possible to evaluate treatment effect moderation and to draw inferences about treatment effects by taking the full utilization of individual covariates from multiple clinical trials. In IPD-NMA, restricted mean survival time (RMST) models have gained popularity when analyzing time-to-event outcomes because RMST models offer more straightforward interpretations of treatment effects with fewer assumptions than hazard ratios commonly estimated from Cox models. Existing approaches estimate RMST within each study and then combine by using aggregate-level NMA methods. However, these methods cannot incorporate individual covariates to evaluate the effect moderation. In this paper, we propose advanced RMST NMA models when IPD are available. Our models allow us to study treatment effect moderation and provide a comprehensive understanding about comparative effectiveness of treatments and subgroup effects. The methods are evaluated by an extensive simulation study and illustrated using a real NMA example about treatments for patients with atrial fibrillation.</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":"143438782","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 Bias-Corrected Bayesian Nonparametric Model for Combining Studies With Varying Quality in Meta-Analysis

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-02-07 DOI: 10.1002/bimj.70034

Pablo Emilio Verde, Gary L. Rosner

{"title":"A Bias-Corrected Bayesian Nonparametric Model for Combining Studies With Varying Quality in Meta-Analysis","authors":"Pablo Emilio Verde, Gary L. Rosner","doi":"10.1002/bimj.70034","DOIUrl":"https://doi.org/10.1002/bimj.70034","url":null,"abstract":"<p>Bayesian nonparametric (BNP) approaches for meta-analysis have been developed to relax distributional assumptions and handle the heterogeneity of random effects distributions. These models account for possible clustering and multimodality of the random effects distribution. However, when we combine studies of varying quality, the resulting posterior is not only a combination of the results of interest but also factors threatening the integrity of the studies' results. We refer to these factors as the studies' <i>internal validity biases</i> (e.g., reporting bias, data quality, and patient selection bias). In this paper, we introduce a new meta-analysis model called the bias-corrected Bayesian nonparametric (BC-BNP) model, which aims to automatically correct for internal validity bias in meta-analysis by only using the reported effects and their standard errors. The BC-BNP model is based on a mixture of a parametric random effects distribution, which represents the model of interest, and a BNP model for the bias component. This model relaxes the parametric assumptions of the bias distribution of the model introduced by Verde. Using simulated data sets, we evaluate the BC-BNP model and illustrate its applications with two real case studies. Our results show several potential advantages of the BC-BNP model: (1) It can detect bias when present while producing results similar to a simple normal–normal random effects model when bias is absent. (2) Relaxing the parametric assumptions of the bias component does not affect the model of interest and yields consistent results with the model of Verde. (3) In some applications, a BNP model of bias offers a better understanding of the studies' biases by clustering studies with similar biases. We implemented the BC-BNP model in the R package jarbes, facilitating its practical application.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362461","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

Mediation Analysis With Exposure–Mediator Interaction and Covariate Measurement Error Under the Additive Hazards Model

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-02-07 DOI: 10.1002/bimj.70035

Ying Yan, Lingzhu Shen

{"title":"Mediation Analysis With Exposure–Mediator Interaction and Covariate Measurement Error Under the Additive Hazards Model","authors":"Ying Yan, Lingzhu Shen","doi":"10.1002/bimj.70035","DOIUrl":"https://doi.org/10.1002/bimj.70035","url":null,"abstract":"<div>\u0000 \u0000 <p>Causal mediation analysis is a useful tool to examine how an exposure variable causally affects an outcome variable through an intermediate variable. In recent years, there is increasing research interest in mediation analysis with survival data. The existing literature usually requires accurate measurements of the mediator and the confounders, which is infeasible in many biomedical and social science studies. Ignoring measurement errors may lead to misleading inference results. Furthermore, the current identification results of causal effects under the additive hazards model are limited to the scenario with no exposure–mediator interaction, which can be unappealing in mediation analysis. In this paper, we derive the identification results of direct and indirect effects under the additive hazards model in the presence of exposure–mediator interaction. Furthermore, we propose a corrected approach to adjust for the impact of measurement error in the mediator and the confounders and obtain consistent estimations of the direct and indirect effects. The performance of the proposed method is studied in simulation studies and a real data study.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362514","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

Multiple Contrast Tests in the Presence of Partial Heteroskedasticity 部分异方差存在下的多重对比检验。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2025-01-13 DOI: 10.1002/bimj.70019

Mario Hasler, Tim Birr, Ludwig A. Hothorn

引用次数: 0