Biometrical Journal最新文献

{"title":"Multivariate Bayesian Dynamic Borrowing for Repeated Measures Data With Application to External Control Arms in Open-Label Extension Studies","authors":"Benjamin F. Hartley,&nbsp;Matthew A. Psioda,&nbsp;Adrian P. Mander","doi":"10.1002/bimj.70079","DOIUrl":"10.1002/bimj.70079","url":null,"abstract":"<p>Borrowing analyses are increasingly important in clinical trials. We develop a method for using robust mixture priors in multivariate dynamic borrowing. The method was motivated by a desire to produce causally valid, long-term treatment effect estimates of a continuous endpoint from a single active-arm open-label extension study following a randomized clinical trial by dynamically incorporating prior beliefs from a long-term external control arm. The proposed method is a generally applicable Bayesian dynamic borrowing analysis for estimates of multivariate summary metrics based on a multivariate normal likelihood function for various parameter models, some of which we describe. There are important connections to estimation incorporating a prior belief for a hypothetical estimand strategy, that is, had the event not occurred, for intercurrent events which lead to missing data.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12504158/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245796","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}

{"title":"Weibull Regression With Both Measurement Error and Misclassification in Covariates","authors":"Zhiqiang Cao,&nbsp;Man Yu Wong","doi":"10.1002/bimj.70083","DOIUrl":"10.1002/bimj.70083","url":null,"abstract":"<div>\u0000 \u0000 <p>The problem of measurement error and misclassification in covariates is ubiquitous in nutritional epidemiology and some other research areas, which often leads to biased estimate and loss of power. However, addressing both measurement error and misclassification simultaneously in a single analysis is challenged and less actively studied, especially in regression model for survival data with censoring. The approximate maximum likelihood estimation (AMLE) has been proved to be an effective method to correct both measurement error and misclassification simultaneously in a logistic regression model. However, its impact on survival analysis models has not been studied. In this paper, we study biases caused by both measurement error and misclassification in covariates from a Weibull accelerated failure time model, and explore the use of AMLE and its asymptotic properties to correct these biases. Extensive simulation studies are conducted to evaluate the finite-sample performance of the resulting estimator. The proposed method is then applied to deal with measurement error and misclassification in some nutrients of interest from the EPIC-InterAct Study.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245843","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}

{"title":"Improving Genomic Prediction Using High-Dimensional Secondary Phenotypes: The Genetic Latent Factor Approach","authors":"Killian A. C. Melsen,&nbsp;Jonathan F. Kunst,&nbsp;José Crossa,&nbsp;Margaret R. Krause,&nbsp;Fred A. van Eeuwijk,&nbsp;Willem Kruijer,&nbsp;Carel F. W. Peeters","doi":"10.1002/bimj.70081","DOIUrl":"10.1002/bimj.70081","url":null,"abstract":"<p>Decreasing costs and new technologies have led to an increase in the amount of data available to plant breeding programs. High-throughput phenotyping (HTP) platforms routinely generate high-dimensional datasets of secondary features that may be used to improve genomic prediction accuracy. However, integration of these data comes with challenges such as multicollinearity, parameter estimation in <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>p</mi>\u0000 <mo>&gt;</mo>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 <annotation>$p &gt; n$</annotation>\u0000 </semantics></math> settings, and the computational complexity of many standard approaches. Several methods have emerged to analyze such data, but interpretation of model parameters often remains challenging. We propose genetic latent factor best linear unbiased prediction (glfBLUP), a prediction pipeline that reduces the dimensionality of the original secondary HTP data using generative factor analysis. In short, glfBLUP uses redundancy filtered and regularized genetic and residual correlation matrices to fit a maximum likelihood factor model and estimate genetic latent factor scores. These latent factors are subsequently used in multitrait genomic prediction. Our approach performs better than alternatives in extensive simulations and a real-world application, while producing easily interpretable and biologically relevant parameters. We discuss several possible extensions and highlight glfBLUP as the basis for a flexible and modular multitrait genomic prediction framework.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12504928/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245751","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}

{"title":"Sharp Bounds for Continuous-Valued Treatment Effects with Unobserved Confounders.","authors":"Jean-Baptiste Baitairian, Bernard Sebastien, Rana Jreich, Sandrine Katsahian, Agathe Guilloux","doi":"10.1002/bimj.70084","DOIUrl":"https://doi.org/10.1002/bimj.70084","url":null,"abstract":"<p><p>In causal inference, treatment effects are typically estimated under the ignorability, or unconfoundedness, assumption, which is often unrealistic in observational data. By relaxing this assumption and conducting a sensitivity analysis, we introduce novel bounds and derive confidence intervals for the Average Potential Outcome (APO)-a standard metric for evaluating continuous-valued treatment or exposure effects. We demonstrate that these bounds are sharp under a continuous sensitivity model, in the sense that they give the smallest possible interval under this model, and propose a doubly robust version of our estimators. In a comparative analysis with another method from the literature, using both simulated and real data sets, we show that our approach not only yields sharper bounds but also achieves good coverage of the true APO, with significantly reduced computation times.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 5","pages":"e70084"},"PeriodicalIF":1.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145294473","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}

{"title":"Federated Mixed Effects Logistic Regression Based on One-Time Shared Summary Statistics","authors":"Marie Analiz April Limpoco,&nbsp;Christel Faes,&nbsp;Niel Hens","doi":"10.1002/bimj.70080","DOIUrl":"10.1002/bimj.70080","url":null,"abstract":"<div>\u0000 \u0000 <p>Upholding data privacy, especially in medical research, has become tantamount to facing difficulties in accessing individual-level patient data. Estimating mixed effects binary logistic regression models involving data from multiple data providers, like hospitals, thus becomes more challenging. Federated learning has emerged as an option to preserve the privacy of individual observations while still estimating a global model that can be interpreted on the individual level, but it usually involves iterative communication between the data providers and the data analyst. In this paper, we present a strategy to estimate a mixed effects binary logistic regression model that requires data providers to share summary statistics only once. It involves generating pseudo-data whose summary statistics match those of the actual data and using these in the model estimation process instead of the actual unavailable data. Our strategy is able to include multiple predictors, which can be a combination of continuous and categorical variables. Through simulation, we show that our approach estimates the true model at least as good as the one that requires the pooled individual observations. An illustrative example using real data is provided. Unlike typical federated learning algorithms, our approach eliminates infrastructure requirements and security issues while being communication efficient and while accounting for heterogeneity.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187761","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}

{"title":"A New Logistic Model With Subject-Specific and Serially Correlated Time-Specific Distribution-Free Random Effects on the Unit Interval for Longitudinal Binary Data","authors":"Lulu Zhang,&nbsp;Renjun Ma,&nbsp;Guohua Yan,&nbsp;Xifen Huang","doi":"10.1002/bimj.70078","DOIUrl":"10.1002/bimj.70078","url":null,"abstract":"<div>\u0000 \u0000 <p>Various beta-binomial mixed effects models have been developed in recent years for longitudinal binary data; however, these approaches rely heavily on the parametric specification of beta and normal random effects. Furthermore, their incorporation of normal random effects into beta-binomial models has been done at the sacrifice of certain computational convenience and clear interpretation with beta-binomial models. In this paper, we introduce a new model that incorporates subject-specific and serially correlated time-specific distribution-free random effects on the unit interval into logistic regression multiplicatively with fixed effects. This new multiplicative model facilitates the interpretation of random effects on the unit interval as risk modifiers. This multiplicative model setup also eases the model derivation and random effects prediction. A quasi-likelihood approach has been developed in the estimation of our model. Our results are robust against random effects distributions. Our method is illustrated through the analysis of multiple sclerosis trial data.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187785","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}

{"title":"ADDIS-Graphs for Online Error Control With Application to Platform Trials","authors":"Lasse Fischer,&nbsp;Marta Bofill Roig,&nbsp;Werner Brannath","doi":"10.1002/bimj.70075","DOIUrl":"10.1002/bimj.70075","url":null,"abstract":"<p>In contemporary research, online error control is often required, where an error criterion, such as familywise error rate (FWER) or false discovery rate (FDR), shall remain under control while testing an a priori unbounded sequence of hypotheses. The existing online literature mainly considered large-scale studies and constructed powerful but rigid algorithms for these. However, smaller studies, such as platform trials, require high flexibility and easy interpretability to take study objectives into account and facilitate the communication. Another challenge in platform trials is that due to the shared control arm some of the <span></span><math>\u0000 <semantics>\u0000 <mi>p</mi>\u0000 <annotation>$p$</annotation>\u0000 </semantics></math>-values are dependent and significance levels need to be prespecified before the decisions for all the past treatments are available. We propose adaptive-discarding-Graphs (ADDIS-Graphs) with FWER control that due to their graphical structure perfectly adapt to such settings and provably uniformly improve the state-of-the-art method. We introduce several extensions of these ADDIS-Graphs, including the incorporation of information about the joint distribution of the <span></span><math>\u0000 <semantics>\u0000 <mi>p</mi>\u0000 <annotation>$p$</annotation>\u0000 </semantics></math>-values and a version for FDR control.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187788","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}

{"title":"Inference Under Covariate-Adaptive Randomization Using Random Center-Effect","authors":"Anjali Pandey,&nbsp;Harsha Shree BS,&nbsp;Andrea Callegaro","doi":"10.1002/bimj.70076","DOIUrl":"https://doi.org/10.1002/bimj.70076","url":null,"abstract":"<div>\u0000 \u0000 <p>The minimization method is a popular choice for covariate-adaptive randomization in multicenter trials. Existing literature suggests that the type-I error is controlled if minimization variables are included in the statistical analysis. However, in practice, minimization variables with many categories, such as the recruitment center, are often not included in the model. In this paper, we propose including the minimization variable “center” as a random effect and assess its performance using simulations for Gaussian, binary, and Poisson endpoint variables. Our simulation study suggests that the random-effect model controls type-I error and preserves maximum power for all three endpoints under varied clinical trial settings. This approach offers an alternative to the re-randomization test, which regulatory authorities often suggest for sensitivity analysis.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110916","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}

{"title":"From Data to Knowledge. Advancing Life Sciences: Editorial for the CEN2023 Special Issue","authors":"Werner Brannath,&nbsp;Frank Bretz,&nbsp;Hans Ulrich Burger,&nbsp;Malgorzata Graczyk,&nbsp;Annette Kopp-Schneider","doi":"10.1002/bimj.70077","DOIUrl":"10.1002/bimj.70077","url":null,"abstract":"&lt;p&gt;This Special Issue—&lt;i&gt;From Data to Knowledge. Advancing Life Sciences&lt;/i&gt;—arose from the Fifth Conference of the Central European Network (CEN2023) of the International Biometric Society, which took place on September 3–7, 2023, in Basel, Switzerland (https://cen2023.github.io/home/). More than 500 colleagues registered for in-person attendance and a further 100 participated virtually, representing more than 30 countries. The scientific program began on Sunday with seven short courses. From Monday through Thursday, the main conference featured seven parallel tracks and nearly 400 oral and poster contributions, including keynote presentations by Ruth Keogh, Alicja Szabelska-Beręsewicz, and Peter Bühlmann.&lt;/p&gt;&lt;p&gt;This special issue consists of 14 peer-reviewed articles generated from research work presented at the symposium. The collection reflects the vibrancy and breadth of current research in biometrics, spanning areas such as clinical trials, epidemiology, genomics, and ecology. Von Felten et al. performed a simulation study comparing multiple approaches to estimating the survivor average causal effect in randomized trials with outcomes truncated by death. Carrozzo et al. compared the statistical efficiency of a two-arm crossover randomized controlled trial with that of a meta-analysis of &lt;i&gt;N&lt;/i&gt;-of-1 studies, highlighting the potential of sequential aggregation. Burk et al. proposed a cooperative penalized regression approach for high-dimensional variable selection with competing risks, improving feature selection over traditional methods. Erdmann et al. demonstrated how multistate modeling of progression-free and overall survival endpoints can enhance oncology clinical trial design, especially in the presence of nonproportional hazards. Wünsch et al. investigated how the flexibility in gene set analysis can lead to overoptimistic findings, raising awareness of methodological uncertainty and offering practical guidance. Nassiri et al. proposed a Bayesian posterior probability adjustment method to mitigate class imbalance in classification tasks, improving predictive accuracy. Kim et al. introduced an inverse-weighted quantile regression approach tailored for partially interval-censored data, applicable to complex biomedical endpoints. Teschke et al. developed a method using cross-leverage scores to efficiently detect interaction effects in high-dimensional genetic data. Uno et al. proposed Firth-type penalized regression methods to improve the performance of modified Poisson and least-squares regression models in small or sparse binary outcome settings. Langthaler et al. developed a nonparametric inference method for assessing ecological niche overlap among multiple species, supporting biodiversity research. Kipruto and Sauerbrei revisited postestimation shrinkage in linear models, introducing a modified parameter-wise shrinkage method and assessing its performance in various settings. Röver and Friede explored the concept of “study twins” ","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076681","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}

{"title":"Estimands for Early-Phase Dose Optimization Trials in Oncology","authors":"Ayon Mukherjee,&nbsp;Jonathan L. Moscovici,&nbsp;Zheng Liu","doi":"10.1002/bimj.70072","DOIUrl":"10.1002/bimj.70072","url":null,"abstract":"<p>Phase I dose escalation trials in oncology generally aim to find the maximum tolerated dose. However, with the advent of molecular-targeted therapies and antibody drug conjugates, dose-limiting toxicities are less frequently observed, giving rise to the concept of optimal biological dose (OBD), which considers both efficacy and toxicity. The estimand framework presented in the addendum of the ICH E9(R1) guidelines strengthens the dialogue between different stakeholders by bringing in greater clarity in the clinical trial objectives and by providing alignment between the targeted estimand under consideration and the statistical analysis methods. However, there is a lack of clarity in implementing this framework in early-phase dose optimization studies. This paper aims to discuss the estimand framework for dose optimization trials in oncology, considering efficacy and toxicity through utility functions. Such trials should include pharmacokinetics data, toxicity data, and efficacy data. Based on these data, the analysis methods used to identify the optimized dose/s are also described. Focusing on optimizing the utility function to estimate the OBD, the population-level summary measure should reflect only the properties used for estimating this utility function. A detailed strategy recommendation for intercurrent events has been provided using a real-life oncology case study. Key recommendations regarding the estimand attributes include that in a seamless phase I/II dose optimization trial, the treatment attribute should start when the subject receives the first dose. We argue that such a framework brings in additional clarity to dose optimization trial objectives and strengthens the understanding of the drug under consideration, which would enable the correct dose to move to phase II of clinical development.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034873","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}