Biometrical Journal最新文献

{"title":"Adjusted Inference for Multiple Testing Procedure in Group-Sequential Designs","authors":"Yujie Zhao,&nbsp;Qi Liu,&nbsp;Linda Z. Sun,&nbsp;Keaven M. Anderson","doi":"10.1002/bimj.70020","DOIUrl":"10.1002/bimj.70020","url":null,"abstract":"<div>\u0000 \u0000 <p>Adjustment of statistical significance levels for repeated analysis in group-sequential trials has been understood for some time. Adjustment accounting for testing multiple hypotheses is also well understood. There is limited research on simultaneously adjusting for both multiple hypothesis testing and repeated analyses of one or more hypotheses. We address this gap by proposing <i>adjusted-sequential p-values</i> that reject when they are less than or equal to the family-wise Type I error rate (FWER). We also propose sequential <span></span><math>\u0000 <semantics>\u0000 <mi>p</mi>\u0000 <annotation>$p$</annotation>\u0000 </semantics></math>-values for intersection hypotheses to compute adjusted-sequential <span></span><math>\u0000 <semantics>\u0000 <mi>p</mi>\u0000 <annotation>$p$</annotation>\u0000 </semantics></math>-values for elementary hypotheses. We demonstrate the application using weighted Bonferroni tests and weighted parametric tests for inference on each elementary hypothesis tested.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840160","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 1'25","authors":"","doi":"10.1002/bimj.70027","DOIUrl":"https://doi.org/10.1002/bimj.70027","url":null,"abstract":"","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"67 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142868580","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":"Detecting Interactions in High-Dimensional Data Using Cross Leverage Scores","authors":"Sven Teschke,&nbsp;Katja Ickstadt,&nbsp;Alexander Munteanu","doi":"10.1002/bimj.70014","DOIUrl":"https://doi.org/10.1002/bimj.70014","url":null,"abstract":"<p>We develop a variable selection method for interactions in regression models on large data in the context of genetics. The method is intended for investigating the influence of single-nucleotide polymorphisms (SNPs) and their interactions on health outcomes, which is a <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>p</mi>\u0000 <mo>≫</mo>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 <annotation>$pgg n$</annotation>\u0000 </semantics></math> problem. We introduce cross leverage scores (CLSs) to detect interactions of variables while maintaining interpretability. Using this method, it is not necessary to consider every possible interaction between variables individually, which would be very time-consuming even for moderate amounts of variables. Instead, we calculate the CLS for each variable and obtain a measure of importance for this variable. Calculating the scores remains time-consuming for large data sets. The key idea for scaling to large data is to divide the data into smaller random batches or consecutive windows of variables. This avoids complex and time-consuming computations on high-dimensional matrices by performing the computations only for small subsets of the data, which is less costly. We compare these methods to provable approximations of CLS based on sketching, which aims at summarizing data succinctly. In a simulation study, we show that the CLSs are directly linked to the importance of a variable in the sense of an interaction effect. We further show that the approximation approaches are appropriate for performing the calculations efficiently on arbitrarily large data while preserving the interaction detection effect of the CLS. This underlines their scalability to genome wide data. In addition, we evaluate the methods on real data from the HapMap project.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 8","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142749303","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":"Model Selection for Ordinary Differential Equations: A Statistical Testing Approach","authors":"Itai Dattner,&nbsp;Shota Gugushvili,&nbsp;Oleksandr Laskorunskyi","doi":"10.1002/bimj.70013","DOIUrl":"10.1002/bimj.70013","url":null,"abstract":"<p>Ordinary differential equations (ODEs) are foundational tools in modeling intricate dynamics across a gamut of scientific disciplines. Yet, a possibility to represent a single phenomenon through multiple ODE models, driven by different understandings of nuances in internal mechanisms or abstraction levels, presents a model selection challenge. This study introduces a testing-based approach for ODE model selection amidst statistical noise. Rooted in the model misspecification framework, we adapt classical statistical paradigms (Vuong and Hotelling) to the ODE context, allowing for the comparison and ranking of diverse causal explanations without the constraints of nested models. Our simulation studies numerically investigate the statistical properties of the test, demonstrating its attainment of the nominal size and power across various settings. Real-world data examples further underscore the algorithm's applicability in practice. To foster accessibility and encourage real-world applications, we provide a user-friendly Python implementation of our model selection algorithm, bridging theoretical advancements with hands-on tools for the scientific community.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 8","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741437","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":"τ\u0000 $tau$\u0000 -Inflated Beta Regression Model for Estimating \u0000 \u0000 τ\u0000 $tau$\u0000 -Restricted Means and Event-Free Probabilities for Censored Time-to-Event Data","authors":"Yizhuo Wang,&nbsp;Susan Murray","doi":"10.1002/bimj.70009","DOIUrl":"10.1002/bimj.70009","url":null,"abstract":"&lt;p&gt;In this research, we propose analysis of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;annotation&gt;$tau$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-restricted censored time-to-event data via a &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;annotation&gt;$tau$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-inflated beta regression (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;annotation&gt;$tau$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-IBR) model. The outcome of interest is &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;min&lt;/mi&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;${rm min}(tau,T)$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, where &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;annotation&gt;$T$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;annotation&gt;$tau$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; are the time-to-event and follow-up duration, respectively. Our analysis goals include estimation and inference related to &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;annotation&gt;$tau$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-restricted mean survival time (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;annotation&gt;$tau$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-RMST) values and event-free probabilities at &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;annotation&gt;$tau$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; that address the censored nature of the data. In this setting, it is common to observe many individuals with &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;min&lt;/mi&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;${rm min}(tau,T)=tau$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, a point mass that is typically overlooked in &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;annotation&gt;$tau$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-restricted event-time analyses. Our proposed &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;τ&lt;/mi&gt;\u0000 &lt;annotation&gt;$tau$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 8","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741342","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":"Risk-Based Decision Making: Estimands for Sequential Prediction Under Interventions","authors":"Kim Luijken,&nbsp;Paweł Morzywołek,&nbsp;Wouter van Amsterdam,&nbsp;Giovanni Cinà,&nbsp;Jeroen Hoogland,&nbsp;Ruth Keogh,&nbsp;Jesse H. Krijthe,&nbsp;Sara Magliacane,&nbsp;Thijs van Ommen,&nbsp;Niels Peek,&nbsp;Hein Putter,&nbsp;Maarten van Smeden,&nbsp;Matthew Sperrin,&nbsp;Junfeng Wang,&nbsp;Daniala L. Weir,&nbsp;Vanessa Didelez,&nbsp;Nan van Geloven","doi":"10.1002/bimj.70011","DOIUrl":"10.1002/bimj.70011","url":null,"abstract":"<p>Prediction models are used among others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: for example, an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred, and reevaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 8","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741439","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 Matched Design for Causal Inference With Survey Data: Evaluation of Medical Marijuana Legalization in Kentucky and Tennessee","authors":"Marco H. Benedetti,&nbsp;Bo Lu,&nbsp;Motao Zhu","doi":"10.1002/bimj.70012","DOIUrl":"10.1002/bimj.70012","url":null,"abstract":"<p>A concern surrounding marijuana legalization is that driving after marijuana use may become more prevalent. Survey data are valuable for estimating policy effects, however their observational nature and unequal sampling probabilities create challenges for causal inference. To estimate population-level effects using survey data, we propose a matched design and implement sensitivity analyses to quantify how robust conclusions are to unmeasured confounding. Both theoretical justification and simulation studies are presented. We found no support that marijuana legalization increased tolerant behaviors and attitudes toward driving after marijuana use, and these conclusions seem moderately robust to unmeasured confounding.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 8","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741434","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":"Domain Selection for Gaussian Process Data: An Application to Electrocardiogram Signals","authors":"Nicolás Hernández,&nbsp;Gabriel Martos","doi":"10.1002/bimj.70018","DOIUrl":"10.1002/bimj.70018","url":null,"abstract":"<p>Gaussian processes and the Kullback–Leibler divergence have been deeply studied in statistics and machine learning. This paper marries these two concepts and introduce the local Kullback–Leibler divergence to learn about intervals where two Gaussian processes differ the most. We address subtleties entailed in the estimation of local divergences and the corresponding interval of local maximum divergence as well. The estimation performance and the numerical efficiency of the proposed method are showcased via a Monte Carlo simulation study. In a medical research context, we assess the potential of the devised tools in the analysis of electrocardiogram signals.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 8","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741435","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 Semiparametric Two-Sample Density Ratio Model With a Change Point","authors":"Jiahui Feng,&nbsp;Kin Yau Wong,&nbsp;Chun Yin Lee","doi":"10.1002/bimj.202300214","DOIUrl":"10.1002/bimj.202300214","url":null,"abstract":"<div>\u0000 \u0000 <p>The logistic regression model for a binary outcome with a continuous covariate can be expressed equivalently as a two-sample density ratio model for the covariate. Utilizing this equivalence, we study a change-point logistic regression model within the corresponding density ratio modeling framework. We investigate estimation and inference methods for the density ratio model and develop maximal score-type tests to detect the presence of a change point. In contrast to existing work, the density ratio modeling framework facilitates the development of a natural Kolmogorov–Smirnov type test to assess the validity of the logistic model assumptions. A simulation study is conducted to evaluate the finite-sample performance of the proposed tests and estimation methods. We illustrate the proposed approach using a mother-to-child HIV-1 transmission data set and an oral cancer data set.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 8","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717664","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":"Smoothed Estimation on Optimal Treatment Regime Under Semisupervised Setting in Randomized Trials","authors":"Xiaoqi Jiao,&nbsp;Mengjiao Peng,&nbsp;Yong Zhou","doi":"10.1002/bimj.70006","DOIUrl":"10.1002/bimj.70006","url":null,"abstract":"<div>\u0000 \u0000 <p>A treatment regime refers to the process of assigning the most suitable treatment to a patient based on their observed information. However, prevailing research on treatment regimes predominantly relies on labeled data, which may lead to the omission of valuable information contained within unlabeled data, such as historical records and healthcare databases. Current semisupervised works for deriving optimal treatment regimes either rely on model assumptions or struggle with high computational burdens for even moderate-dimensional covariates. To address this concern, we propose a semisupervised framework that operates within a model-free context to estimate the optimal treatment regime by leveraging the abundant unlabeled data. Our proposed approach encompasses three key steps. First, we employ a single-index model to achieve dimension reduction, followed by kernel regression to impute the missing outcomes in the unlabeled data. Second, we propose various forms of semisupervised value functions based on the imputed values, incorporating both labeled and unlabeled data components. Lastly, the optimal treatment regimes are derived by maximizing the semisupervised value functions. We establish the consistency and asymptotic normality of the estimators proposed in our framework. Furthermore, we introduce a perturbation resampling procedure to estimate the asymptotic variance. Simulations confirm the advantageous properties of incorporating unlabeled data in the estimation for optimal treatment regimes. A practical data example is also provided to illustrate the application of our methodology. This work is rooted in the framework of randomized trials, with additional discussions extending to observational studies.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 8","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696123","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}