Biometrical Journal最新文献

{"title":"Flexible Parametric Accelerated Failure Time Models With Cure","authors":"Birzhan Akynkozhayev,&nbsp;Benjamin Christoffersen,&nbsp;Xingrong Liu,&nbsp;Keith Humphreys,&nbsp;Mark Clements","doi":"10.1002/bimj.70074","DOIUrl":"10.1002/bimj.70074","url":null,"abstract":"<p>Accelerated failure time (AFT) models offer an attractive alternative to Cox proportional hazards models. AFT models are collapsible and, unlike hazard ratios in proportional hazards models, the acceleration factor—a key effect measure in AFT models—is collapsible, meaning its value remains unchanged when adjusting for additional covariates. In addition, AFT models provide an intuitive interpretation directly on the survival time scale. From the recent development of smooth parametric AFT models, we identify potential issues with their applications and note several desired extensions that have not yet been implemented. To enrich this tool and its application in clinical research, we improve the AFT models within a flexible parametric framework in several ways: we adopt monotone natural splines to constrain the log cumulative hazard to be a monotonic function across its support; allow for time-varying acceleration factors, possibly include cure and accommodating more than one time-varying effect; and implement both mixture and nonmixture cure models. We implement all of these extensions in the <span>rstpm2</span> package, which is publicly available on CRAN. Simulations highlight a varying success in estimating cure fractions. However, in terms of covariate-effect estimation, flexible AFT models appear to be more robust than the Cox model even when there is a high proportion of cured individuals in the data, regardless of whether cure is reached within the observed data. We also apply some of our extensions of AFT models to real-world survival data.</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.70074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034844","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":"Hazards, Causality, and Practical Relevance of Collider Effects – Comment on Beyersmann et al. “Hazards Constitute Key Quantities for Analyzing, Interpreting and Understanding Time-to-Event Data”","authors":"Ralf Bender,&nbsp;Lars Beckmann","doi":"10.1002/bimj.70071","DOIUrl":"https://doi.org/10.1002/bimj.70071","url":null,"abstract":"<div>\u0000 \u0000 <p>Hazards constitute key quantities for analyzing, interpreting, and understanding time-to-event data. Hazards and corresponding effect measures, such as the hazard ratio from the Cox proportional hazards model, have a valid causal interpretation if the hazard function is considered as a function in time rather than hazards at specific time points. In this comment, we would like to add two points: (1) The hazard ratio is also a useful population-level estimand with a valid causal interpretation. (2) Empirical evidence shows that problematic situations, which could occur in theory due to strong heterogeneity, are usually avoided in typical randomized controlled trials.</p>\u0000 </div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145012218","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":"Issue Information: Biometrical Journal 5'25","authors":"","doi":"10.1002/bimj.70073","DOIUrl":"https://doi.org/10.1002/bimj.70073","url":null,"abstract":"","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905568","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":"Unified Estimation Method for Partially Linear Models With Nonmonotone Missing at Random Data","authors":"Yang Zhao","doi":"10.1002/bimj.70070","DOIUrl":"https://doi.org/10.1002/bimj.70070","url":null,"abstract":"<p>Partially linear models are commonly used in observational studies of the causal effect of treatment and/or exposure when there are observed confounding variables. The models are robust and asymptotically distribution-free for testing the causal null hypothesis. In this research, we investigate methods for estimating the partially linear models with data missing at random in all the variables, including the response, the treatment, and the confounding variables. We develop a general estimation method for inference in partially linear models with nonmonotone missing at random data. It proposes using partially linear working models to improve the estimation efficiency of the standard complete case method. It can be shown that the new estimator is consistent, which does not depend on the correctness of the working models. In addition, we recommend bootstrap estimates for the asymptotic variances and semiparametric models for the missing data probabilities. It is computationally simple and can be directly implemented in standard software. Simulation studies are provided to examine its performance. A real data example with sparsely observed missingness patterns is used to illustrate the method.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905397","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":"A Bayesian Basket Trial Design Using Local Power Prior","authors":"Haiming Zhou,&nbsp;Rex Shen,&nbsp;Sutan Wu,&nbsp;Philip He","doi":"10.1002/bimj.70069","DOIUrl":"https://doi.org/10.1002/bimj.70069","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent years, basket trials, which allow the evaluation of an experimental therapy across multiple tumor types within a single protocol, have gained prominence in early-phase oncology development. Unlike traditional trials, which evaluate each tumor type separately and often face challenges with limited sample sizes, basket trials offer the advantage of borrowing information across various tumor types to enhance statistical power. However, a key challenge in designing basket trials is determining the appropriate extent of information borrowing while maintaining an acceptable type I error rate control. In this paper, we propose a novel three-component local power prior (local-PP) framework that introduces a dynamic and flexible approach to information borrowing. The framework consists of three components: global borrowing control, pairwise similarity assessments, and a borrowing threshold, allowing for tailored and interpretable borrowing across heterogeneous tumor types. Unlike many existing Bayesian methods that rely on computationally intensive Markov chain Monte Carlo (MCMC) sampling, the proposed approach provides a closed-form solution, significantly reducing computation time in large-scale simulations for evaluating operating characteristics. Extensive simulations demonstrate that the proposed local-PP framework performs comparably to more complex methods while significantly shortening computation time.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773484","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":"Using Machine Learning to Improve Control for Confounding in the Dynamic Weighted Ordinary Least Squares Estimator of Optimal Adaptive Treatment Strategies","authors":"Kossi Clément Trenou,&nbsp;Miceline Mésidor,&nbsp;Aida Eslami,&nbsp;Hermann Nabi,&nbsp;Caroline Diorio,&nbsp;Denis Talbot","doi":"10.1002/bimj.70068","DOIUrl":"https://doi.org/10.1002/bimj.70068","url":null,"abstract":"<p>Estimating optimal adaptive treatment strategies (ATSs) can be done in several ways, including dynamic weighted ordinary least squares (dWOLS). This approach is doubly robust as it requires modeling both the treatment and the response, but only one of those models needs to be correctly specified to obtain a consistent estimator. For estimating an average treatment effect, doubly robust methods have been shown to combine better with machine learning methods than alternatives. However, the use of machine learning within dWOLS has not yet been investigated. Using simulation studies, we evaluate and compare the performance of the dWOLS estimator when the treatment probability is estimated either using machine learning algorithms or a logistic regression model. We further investigate the use of an adaptive <span></span><math>\u0000 <semantics>\u0000 <mi>m</mi>\u0000 <annotation>$m$</annotation>\u0000 </semantics></math>-out-of-<span></span><math>\u0000 <semantics>\u0000 <mi>n</mi>\u0000 <annotation>$n$</annotation>\u0000 </semantics></math> bootstrap method for producing inferences. SuperLearner performed at least as well as logistic regression in terms of bias and variance in scenarios with simple data-generating models and often had improved performance in more complex scenarios. Moreover, the <span></span><math>\u0000 <semantics>\u0000 <mi>m</mi>\u0000 <annotation>$m$</annotation>\u0000 </semantics></math>-out-of-<span></span><math>\u0000 <semantics>\u0000 <mi>n</mi>\u0000 <annotation>$n$</annotation>\u0000 </semantics></math> bootstrap produced confidence intervals with nominal coverage probabilities for parameters that were estimated with low bias. We also apply our proposed approach to the data from a breast cancer registry in Québec, Canada, to estimate an optimal ATS to personalize the use of hormonal therapy in breast cancer patients. Our method is implemented in the <span>R software</span> and available on GitHub https://github.com/kosstre20/MachineLearningToControlConfoundingPersonalizedMedicine.git. We recommend routine use of machine learning to model treatment within dWOLS, at least as a sensitivity analysis for the point estimates.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716817","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":"Rethinking Probability of Success as Bayes Utility","authors":"Fulvio De Santis,&nbsp;Stefania Gubbiotti,&nbsp;Francesco Mariani","doi":"10.1002/bimj.70067","DOIUrl":"https://doi.org/10.1002/bimj.70067","url":null,"abstract":"<p>In the hybrid frequentist-Bayesian approach, the probability of success (PoS) of a trial is the expected value of the traditional power function of a test with respect to a design prior assigned to the parameter under scrutiny. However, this definition is not univocal and some of the proposals do not lack of potential drawbacks. These problems are related to the fact that such definitions are all based on the probability of rejecting the null hypothesis rather than on the probability of choosing the correct hypothesis, be it the null or the alternative. In this article, we propose a unifying, decision-theoretic approach that yields a new definition of PoS as the expected utility of the trial (u-PoS), that is, as the expected probability of making the correct choice between the two hypotheses. This proposal shows a conceptual advantage over previous definitions of PoS; moreover, it produces smaller optimal sample sizes whenever the design prior assigns positive probability to the null hypothesis.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624723","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":"Early and Late Buzzards: Comparing Different Approaches for Quantile-Based Multiple Testing in Heavy-Tailed Wildlife Research Data","authors":"Marléne Baumeister,&nbsp;Merle Munko,&nbsp;Kai-Philipp Gladow,&nbsp;Marc Ditzhaus,&nbsp;Nayden Chakarov,&nbsp;Markus Pauly","doi":"10.1002/bimj.70065","DOIUrl":"https://doi.org/10.1002/bimj.70065","url":null,"abstract":"<p>In medical, ecological, and psychological research, there is a need for methods to handle multiple testing, for example, to consider group comparisons with more than two groups. Typical approaches that deal with multiple testing are mean- or variance-based which can be less effective in the context of heavy-tailed and skewed data. Here, the median is the preferred measure of location and the interquartile range (IQR) is an adequate alternative to the variance. Therefore, it may be fruitful to formulate research questions of interest in terms of the median or the IQR. For this reason, we compare different inference approaches for two-sided and noninferiority hypotheses formulated in terms of medians or IQRs in an extensive simulation study. We consider multiple contrast testing procedures combined with a bootstrap method as well as testing procedures with Bonferroni correction. As an example of a multiple testing problem based on heavy-tailed data, we analyze an ecological trait variation in early and late breeding in a medium-sized bird of prey.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144558058","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":"Validation of a Longitudinal Marker as a Surrogate Using Mediation Analysis and Joint Modeling: Evolution of the PSA as a Surrogate of the Disease-Free Survival","authors":"Quentin Le Coent,&nbsp;Catherine Legrand,&nbsp;James J. Dignam,&nbsp;Virginie Rondeau","doi":"10.1002/bimj.70064","DOIUrl":"https://doi.org/10.1002/bimj.70064","url":null,"abstract":"<p>Longitudinal biomarkers constitute a broad class of potential surrogate endpoints in clinical trials. Several approaches have been proposed for surrogate validation but available methods for validating a longitudinal biomarker as a surrogate of a time-to-event endpoint such as death remain limited. In this work, we propose a method for validating a longitudinal outcome as a surrogate of a time-to-event endpoint using a combination of joint modeling and mediation analysis. The proportion of the total treatment effect on the time-to-event endpoint due to its effect on the biomarker is used as a surrogacy measure. This method is developed to integrate meta-analytic data using a joint model with random effects at both the individual and trial levels. From this model, the indirect treatment effect through the surrogate as well as the direct and total treatment effects is derived using a mediation formula. A simulation study was designed to evaluate the performance of this approach. We applied this method to a multicentric study on prostate cancer to investigate the use of prostate-specific antigen level as a surrogate for disease-free survival.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503158","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":"Issue Information: Biometrical Journal 4'25","authors":"","doi":"10.1002/bimj.70066","DOIUrl":"https://doi.org/10.1002/bimj.70066","url":null,"abstract":"","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 4","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503081","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}