Biometrical Journal最新文献_第5页

Domain Selection for Gaussian Process Data: An Application to Electrocardiogram Signals 高斯过程数据的领域选择：心电图信号的应用

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-11-28 DOI: 10.1002/bimj.70018

Nicolás Hernández, Gabriel Martos

引用次数: 0

A Semiparametric Two-Sample Density Ratio Model With a Change Point 带变化点的半参数双样本密度比模型

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-11-25 DOI: 10.1002/bimj.202300214

Jiahui Feng, Kin Yau Wong, Chun Yin Lee

引用次数: 0

Smoothed Estimation on Optimal Treatment Regime Under Semisupervised Setting in Randomized Trials 随机试验中半监督设置下最佳治疗方案的平滑估计

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-11-23 DOI: 10.1002/bimj.70006

Xiaoqi Jiao, Mengjiao Peng, Yong Zhou

{"title":"Smoothed Estimation on Optimal Treatment Regime Under Semisupervised Setting in Randomized Trials","authors":"Xiaoqi Jiao, Mengjiao Peng, Yong Zhou","doi":"10.1002/bimj.70006","DOIUrl":"10.1002/bimj.70006","url":null,"abstract":"<div>\u0000 \u0000 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.</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}

引用次数: 0

Simulating Data From Marginal Structural Models for a Survival Time Outcome 模拟生存时间结果的边际结构模型数据。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-11-23 DOI: 10.1002/bimj.70010

Shaun R. Seaman, Ruth H. Keogh

{"title":"Simulating Data From Marginal Structural Models for a Survival Time Outcome","authors":"Shaun R. Seaman, Ruth H. Keogh","doi":"10.1002/bimj.70010","DOIUrl":"10.1002/bimj.70010","url":null,"abstract":"Marginal structural models (MSMs) are often used to estimate causal effects of treatments on survival time outcomes from observational data when time-dependent confounding may be present. They can be fitted using, for example, inverse probability of treatment weighting (IPTW). It is important to evaluate the performance of statistical methods in different scenarios, and simulation studies are a key tool for such evaluations. In such simulation studies, it is common to generate data in such a way that the model of interest is correctly specified, but this is not always straightforward when the model of interest is for potential outcomes, as is an MSM. Methods have been proposed for simulating from MSMs for a survival outcome, but these methods impose restrictions on the data-generating mechanism. Here, we propose a method that overcomes these restrictions. The MSM can be, for example, a marginal structural logistic model for a discrete survival time or a Cox or additive hazards MSM for a continuous survival time. The hazard of the potential survival time can be conditional on baseline covariates, and the treatment variable can be discrete or continuous. We illustrate the use of the proposed simulation algorithm by carrying out a brief simulation study. This study compares the coverage of confidence intervals calculated in two different ways for causal effect estimates obtained by fitting an MSM via IPTW.","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":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696121","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

Conditional Variable Screening for Ultra-High Dimensional Longitudinal Data With Time Interactions 对具有时间交互作用的超高维纵向数据进行条件变量筛选。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-11-23 DOI: 10.1002/bimj.70005

Andrea Bratsberg, Abhik Ghosh, Magne Thoresen

{"title":"Conditional Variable Screening for Ultra-High Dimensional Longitudinal Data With Time Interactions","authors":"Andrea Bratsberg, Abhik Ghosh, Magne Thoresen","doi":"10.1002/bimj.70005","DOIUrl":"10.1002/bimj.70005","url":null,"abstract":"In recent years, we have been able to gather large amounts of genomic data at a fast rate, creating situations where the number of variables greatly exceeds the number of observations. In these situations, most models that can handle a moderately high dimension will now become computationally infeasible or unstable. Hence, there is a need for a prescreening of variables to reduce the dimension efficiently and accurately to a more moderate scale. There has been much work to develop such screening procedures for independent outcomes. However, much less work has been done for high-dimensional longitudinal data in which the observations can no longer be assumed to be independent. In addition, it is of interest to capture possible interactions between the genomic variable and time in many of these longitudinal studies. In this work, we propose a novel conditional screening procedure that ranks variables according to the likelihood value at the maximum likelihood estimates in a marginal linear mixed model, where the genomic variable and its interaction with time are included in the model. This is to our knowledge the first conditional screening approach for clustered data. We prove that this approach enjoys the sure screening property, and assess the finite sample performance of the method through simulations.","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":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696119","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

Incompletely Observed Nonparametric Factorial Designs With Repeated Measurements: A Wild Bootstrap Approach 具有重复测量的不完全观测非参数因子设计：野性引导法

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-11-23 DOI: 10.1002/bimj.70008

Lubna Amro, Frank Konietschke, Markus Pauly

{"title":"Incompletely Observed Nonparametric Factorial Designs With Repeated Measurements: A Wild Bootstrap Approach","authors":"Lubna Amro, Frank Konietschke, Markus Pauly","doi":"10.1002/bimj.70008","DOIUrl":"10.1002/bimj.70008","url":null,"abstract":"In many life science experiments or medical studies, subjects are repeatedly observed and measurements are collected in factorial designs with multivariate data. The analysis of such multivariate data is typically based on multivariate analysis of variance (MANOVA) or mixed models, requiring complete data, and certain assumption on the underlying parametric distribution such as continuity or a specific covariance structure, for example, compound symmetry. However, these methods are usually not applicable when discrete data or even ordered categorical data are present. In such cases, nonparametric rank-based methods that do not require stringent distributional assumptions are the preferred choice. However, in the multivariate case, most rank-based approaches have only been developed for complete observations. It is the aim of this work to develop asymptotic correct procedures that are capable of handling missing values, allowing for singular covariance matrices and are applicable for ordinal or ordered categorical data. This is achieved by applying a wild bootstrap procedure in combination with quadratic form-type test statistics. Beyond proving their asymptotic correctness, extensive simulation studies validate their applicability for small samples. Finally, two real data examples are analyzed.","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":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696120","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

Addressing Class Imbalance in Bayesian Classification Through Posterior Probability Adjustment 通过后验概率调整解决贝叶斯分类中的类不平衡问题

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-11-18 DOI: 10.1002/bimj.70004

Vahid Nassiri, Fetene Tekle, Kanaka Tatikola, Helena Geys

引用次数: 0

Inverse-Weighted Quantile Regression With Partially Interval-Censored Data 使用部分区间删失数据的反加权定量回归

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-11-14 DOI: 10.1002/bimj.70001

Yeji Kim, Taehwa Choi, Seohyeon Park, Sangbum Choi, Dipankar Bandyopadhyay

{"title":"Inverse-Weighted Quantile Regression With Partially Interval-Censored Data","authors":"Yeji Kim, Taehwa Choi, Seohyeon Park, Sangbum Choi, Dipankar Bandyopadhyay","doi":"10.1002/bimj.70001","DOIUrl":"10.1002/bimj.70001","url":null,"abstract":"This paper introduces a novel approach to estimating censored quantile regression using inverse probability of censoring weighted (IPCW) methodology, specifically tailored for data sets featuring partially interval-censored data. Such data sets, often encountered in HIV/AIDS and cancer biomedical research, may include doubly censored (DC) and partly interval-censored (PIC) endpoints. DC responses involve either left-censoring or right-censoring alongside some exact failure time observations, while PIC responses are subject to interval-censoring. Despite the existence of complex estimating techniques for interval-censored quantile regression, we propose a simple and intuitive IPCW-based method, easily implementable by assigning suitable inverse-probability weights to subjects with exact failure time observations. The resulting estimator exhibits asymptotic properties, such as uniform consistency and weak convergence, and we explore an augmented-IPCW (AIPCW) approach to enhance efficiency. In addition, our method can be adapted for multivariate partially interval-censored data. Simulation studies demonstrate the new procedure's strong finite-sample performance. We illustrate the practical application of our approach through an analysis of progression-free survival endpoints in a phase III clinical trial focusing on metastatic colorectal cancer.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 8","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632702","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

Mixture Cure Semiparametric Accelerated Failure Time Models With Partly Interval-Censored Data 具有部分区间缺失数据的混合物验证半参数加速失效时间模型

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-11-07 DOI: 10.1002/bimj.202300203

Isabel Li, Jun Ma, Benoit Liquet

{"title":"Mixture Cure Semiparametric Accelerated Failure Time Models With Partly Interval-Censored Data","authors":"Isabel Li, Jun Ma, Benoit Liquet","doi":"10.1002/bimj.202300203","DOIUrl":"10.1002/bimj.202300203","url":null,"abstract":"<div>\u0000 \u0000 In practical survival analysis, the situation of no event for a patient can arise even after a long period of waiting time, which means a portion of the population may never experience the event of interest. Under this circumstance, one remedy is to adopt a mixture cure Cox model to analyze the survival data. However, if there clearly exhibits an acceleration (or deceleration) factor among their survival times, then an accelerated failure time (AFT) model will be preferred, leading to a mixture cure AFT model. In this paper, we consider a penalized likelihood method to estimate the mixture cure semiparametric AFT models, where the unknown baseline hazard is approximated using Gaussian basis functions. We allow partly interval-censored survival data which can include event times and left-, right-, and interval-censoring times. The penalty function helps to achieve a smooth estimate of the baseline hazard function. We will also provide asymptotic properties to the estimates so that inferences can be made on regression parameters and hazard-related quantities. Simulation studies are conducted to evaluate the model performance, which includes a comparative study with an existing method from the smcure R package. The results show that our proposed penalized likelihood method has acceptable performance in general and produces less bias when faced with the identifiability issue compared to smcure. To illustrate the application of our method, a real case study involving melanoma recurrence is conducted and reported. Our model is implemented in our R package aftQnp which is available from https://github.com/Isabellee4555/aftQnP.</div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 8","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142591291","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

The Replication of Equivalence Studies 等效研究的复制。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-10-29 DOI: 10.1002/bimj.202300232

Charlotte Micheloud, Leonhard Held

引用次数: 0