Biometrical Journal最新文献_第3页

False Discovery Rate Control for Lesion-Symptom Mapping With Heterogeneous Data via Weighted p-Values 通过加权 p 值控制异构数据病变-症状映射的错误发现率

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-08-20 DOI: 10.1002/bimj.202300198

Siyu Zheng, Alexander C. McLain, Joshua Habiger, Christopher Rorden, Julius Fridriksson

{"title":"False Discovery Rate Control for Lesion-Symptom Mapping With Heterogeneous Data via Weighted p-Values","authors":"Siyu Zheng, Alexander C. McLain, Joshua Habiger, Christopher Rorden, Julius Fridriksson","doi":"10.1002/bimj.202300198","DOIUrl":"10.1002/bimj.202300198","url":null,"abstract":"<p>Lesion-symptom mapping studies provide insight into what areas of the brain are involved in different aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is nonuniformly distributed, with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Recent advancements in multiple testing methodologies allow researchers to weigh hypotheses according to side information (e.g., information on power heterogeneity). In this paper, we propose the use of <i>p</i>-value weighting for voxel-based lesion-symptom mapping studies. The weights are created using the distribution of lesion status and spatial information to estimate different non-null prior probabilities for each hypothesis test through some common approaches. We provide a <i>monotone minimum weight</i> criterion, which requires minimum a priori power information. Our methods are demonstrated on dependent simulated data and an aphasia study investigating which regions of the brain are associated with the severity of language impairment among stroke survivors. The results demonstrate that the proposed methods have robust error control and can increase power. Further, we showcase how weights can be used to identify regions that are inconclusive due to lack of power.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005983","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

Random Survival Forests With Competing Events: A Subdistribution-Based Imputation Approach 具有竞争事件的随机生存森林：基于子分布的估算方法

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-08-20 DOI: 10.1002/bimj.202400014

Charlotte Behning, Alexander Bigerl, Marvin N. Wright, Peggy Sekula, Moritz Berger, Matthias Schmid

{"title":"Random Survival Forests With Competing Events: A Subdistribution-Based Imputation Approach","authors":"Charlotte Behning, Alexander Bigerl, Marvin N. Wright, Peggy Sekula, Moritz Berger, Matthias Schmid","doi":"10.1002/bimj.202400014","DOIUrl":"10.1002/bimj.202400014","url":null,"abstract":"<p>Random survival forests (RSF) can be applied to many time-to-event research questions and are particularly useful in situations where the relationship between the independent variables and the event of interest is rather complex. However, in many clinical settings, the occurrence of the event of interest is affected by competing events, which means that a patient can experience an outcome other than the event of interest. Neglecting the competing event (i.e., regarding competing events as censoring) will typically result in biased estimates of the cumulative incidence function (CIF). A popular approach for competing events is Fine and Gray's subdistribution hazard model, which directly estimates the CIF by fitting a single-event model defined on a subdistribution timescale. Here, we integrate concepts from the subdistribution hazard modeling approach into the RSF. We develop several imputation strategies that use weights as in a discrete-time subdistribution hazard model to impute censoring times in cases where a competing event is observed. Our simulations show that the CIF is well estimated if the imputation already takes place outside the forest on the overall dataset. Especially in settings with a low rate of the event of interest or a high censoring rate, competing events must not be neglected, that is, treated as censoring. When applied to a real-world epidemiological dataset on chronic kidney disease, the imputation approach resulted in highly plausible predictor–response relationships and CIF estimates of renal events.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202400014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005984","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

Semiparametric Additive Modeling of the Restricted Mean Survival Time 受限平均生存时间的半参数加法模型

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-08-16 DOI: 10.1002/bimj.202200371

Yuan Zhang, Douglas E. Schaubel

{"title":"Semiparametric Additive Modeling of the Restricted Mean Survival Time","authors":"Yuan Zhang, Douglas E. Schaubel","doi":"10.1002/bimj.202200371","DOIUrl":"10.1002/bimj.202200371","url":null,"abstract":"<div>\u0000 \u0000 <p>Analysis of the restricted mean survival time (RMST) has become increasingly common in biomedical studies during the last decade as a means of estimating treatment or covariate effects on survival. Advantages of RMST over the hazard ratio (HR) include increased interpretability and lack of reliance on the often tenuous proportional hazards assumption. Some authors have argued that RMST regression should generally be the frontline analysis as opposed to methods based on counting process increments. However, in order for the use of the RMST to be more mainstream, it is necessary to broaden the range of data structures to which pertinent methods can be applied. In this report, we address this issue from two angles. First, most of existing methodological development for directly modeling RMST has focused on multiplicative models. An additive model may be preferred due to goodness of fit and/or parameter interpretation. Second, many settings encountered nowadays feature high-dimensional categorical (nuisance) covariates, for which parameter estimation is best avoided. Motivated by these considerations, we propose stratified additive models for direct RMST analysis. The proposed methods feature additive covariate effects. Moreover, nuisance factors can be factored out of the estimation, akin to stratification in Cox regression, such that focus can be appropriately awarded to the parameters of chief interest. Large-sample properties of the proposed estimators are derived, and a simulation study is performed to assess finite-sample performance. In addition, we provide techniques for evaluating a fitted model with respect to risk discrimination and predictive accuracy. The proposed methods are then applied to liver transplant data to estimate the effects of donor characteristics on posttransplant survival time.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989558","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

Sample Size Calculation Under Nonproportional Hazards Using Average Hazard Ratios 使用平均危险比计算非比例危险下的样本量。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-08-12 DOI: 10.1002/bimj.202300271

Ina Dormuth, Markus Pauly, Geraldine Rauch, Carolin Herrmann

{"title":"Sample Size Calculation Under Nonproportional Hazards Using Average Hazard Ratios","authors":"Ina Dormuth, Markus Pauly, Geraldine Rauch, Carolin Herrmann","doi":"10.1002/bimj.202300271","DOIUrl":"10.1002/bimj.202300271","url":null,"abstract":"<p>Many clinical trials assess time-to-event endpoints. To describe the difference between groups in terms of time to event, we often employ hazard ratios. However, the hazard ratio is only informative in the case of proportional hazards (PHs) over time. There exist many other effect measures that do not require PHs. One of them is the average hazard ratio (AHR). Its core idea is to utilize a time-dependent weighting function that accounts for time variation. Though propagated in methodological research papers, the AHR is rarely used in practice. To facilitate its application, we unfold approaches for sample size calculation of an AHR test. We assess the reliability of the sample size calculation by extensive simulation studies covering various survival and censoring distributions with proportional as well as nonproportional hazards (N-PHs). The findings suggest that a simulation-based sample size calculation approach can be useful for designing clinical trials with N-PHs. Using the AHR can result in increased statistical power to detect differences between groups with more efficient sample sizes.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300271","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918192","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

Adaptive Multiple Comparisons With the Best 最佳自适应多重比较。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-08-10 DOI: 10.1002/bimj.202300242

Haoyu Chen, Werner Brannath, Andreas Futschik

引用次数: 0

A New Mixture Model With Cure Rate Applied to Breast Cancer Data 应用于乳腺癌数据的带有治愈率的新混合模型。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-08-05 DOI: 10.1002/bimj.202300257

Diego I. Gallardo, Márcia Brandão, Jeremias Leão, Marcelo Bourguignon, Vinicius Calsavara

{"title":"A New Mixture Model With Cure Rate Applied to Breast Cancer Data","authors":"Diego I. Gallardo, Márcia Brandão, Jeremias Leão, Marcelo Bourguignon, Vinicius Calsavara","doi":"10.1002/bimj.202300257","DOIUrl":"10.1002/bimj.202300257","url":null,"abstract":"<div>\u0000 \u0000 <p>We introduce a new modelling for long-term survival models, assuming that the number of competing causes follows a mixture of Poisson and the Birnbaum-Saunders distribution. In this context, we present some statistical properties of our model and demonstrate that the promotion time model emerges as a limiting case. We delve into detailed discussions of specific models within this class. Notably, we examine the expected number of competing causes, which depends on covariates. This allows for direct modeling of the cure rate as a function of covariates. We present an Expectation-Maximization (EM) algorithm for parameter estimation, to discuss the estimation via maximum likelihood (ML) and provide insights into parameter inference for this model. Additionally, we outline sufficient conditions for ensuring the consistency and asymptotic normal distribution of ML estimators. To evaluate the performance of our estimation method, we conduct a Monte Carlo simulation to provide asymptotic properties and a power study of LR test by contrasting our methodology against the promotion time model. To demonstrate the practical applicability of our model, we apply it to a real medical dataset from a population-based study of incidence of breast cancer in São Paulo, Brazil. Our results illustrate that the proposed model can outperform traditional approaches in terms of model fitting, highlighting its potential utility in real-world scenarios.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894971","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

Health Care Provider Clustering Using Fusion Penalty in Quasi-Likelihood 利用准可能性中的融合惩罚对医疗服务提供者进行聚类。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-08-05 DOI: 10.1002/bimj.202300185

Lili Liu, Kevin He, Di Wang, Shujie Ma, Annie Qu, Yihui Luan, J. Philip Miller, Yizhe Song, Lei Liu

{"title":"Health Care Provider Clustering Using Fusion Penalty in Quasi-Likelihood","authors":"Lili Liu, Kevin He, Di Wang, Shujie Ma, Annie Qu, Yihui Luan, J. Philip Miller, Yizhe Song, Lei Liu","doi":"10.1002/bimj.202300185","DOIUrl":"10.1002/bimj.202300185","url":null,"abstract":"<div>\u0000 \u0000 <p>There has been growing research interest in developing methodology to evaluate the health care providers' performance with respect to a patient outcome. Random and fixed effects models are traditionally used for such a purpose. We propose a new method, using a fusion penalty to cluster health care providers based on quasi-likelihood. Without any priori knowledge of grouping information, our method provides a desirable data-driven approach for automatically clustering health care providers into different groups based on their performance. Further, the quasi-likelihood is more flexible and robust than the regular likelihood in that no distributional assumption is needed. An efficient alternating direction method of multipliers algorithm is developed to implement the proposed method. We show that the proposed method enjoys the oracle properties; namely, it performs as well as if the true group structure were known in advance. The consistency and asymptotic normality of the estimators are established. Simulation studies and analysis of the national kidney transplant registry data demonstrate the utility and validity of our method.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141890997","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

Analysis of Nonconcurrent Controls in Adaptive Platform Trials: Separating Randomized and Nonrandomized Information 自适应平台试验中的非同期对照分析：分离随机和非随机信息。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-08-05 DOI: 10.1002/bimj.202300334

Ian C. Marschner, I. Manjula Schou

{"title":"Analysis of Nonconcurrent Controls in Adaptive Platform Trials: Separating Randomized and Nonrandomized Information","authors":"Ian C. Marschner, I. Manjula Schou","doi":"10.1002/bimj.202300334","DOIUrl":"10.1002/bimj.202300334","url":null,"abstract":"<p>Adaptive platform trials allow treatments to be added or dropped during the study, meaning that the control arm may be active for longer than the experimental arms. This leads to nonconcurrent controls, which provide nonrandomized information that may increase efficiency but may introduce bias from temporal confounding and other factors. Various methods have been proposed to control confounding from nonconcurrent controls, based on adjusting for time period. We demonstrate that time adjustment is insufficient to prevent bias in some circumstances where nonconcurrent controls are present in adaptive platform trials, and we propose a more general analytical framework that accounts for nonconcurrent controls in such circumstances. We begin by defining nonconcurrent controls using the concept of a concurrently randomized cohort, which is a subgroup of participants all subject to the same randomized design. We then use cohort adjustment rather than time adjustment. Due to flexibilities in platform trials, more than one randomized design may be in force at any time, meaning that cohort-adjusted and time-adjusted analyses may be quite different. Using simulation studies, we demonstrate that time-adjusted analyses may be biased while cohort-adjusted analyses remove this bias. We also demonstrate that the cohort-adjusted analysis may be interpreted as a synthesis of randomized and indirect comparisons analogous to mixed treatment comparisons in network meta-analysis. This allows the use of network meta-analysis methodology to separate the randomized and nonrandomized components and to assess their consistency. Whenever nonconcurrent controls are used in platform trials, the separate randomized and indirect contributions to the treatment effect should be presented.</p>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202300334","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894972","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

MTML: An Efficient Multitrait Multilocus GWAS Method Based on the Cauchy Combination Test MTML：基于考奇组合检验的高效多特征多焦点 GWAS 方法

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-30 DOI: 10.1002/bimj.202300130

Hongping Guo, Tong Li, Yao Shi, Xiao Wang

{"title":"MTML: An Efficient Multitrait Multilocus GWAS Method Based on the Cauchy Combination Test","authors":"Hongping Guo, Tong Li, Yao Shi, Xiao Wang","doi":"10.1002/bimj.202300130","DOIUrl":"10.1002/bimj.202300130","url":null,"abstract":"<div>\u0000 \u0000 <p>Genome-wide association study (GWAS) by measuring the joint effect of multiple loci on multiple traits, has recently attracted interest, due to the decreased costs of high-throughput genotyping and phenotyping technologies. Previous studies mainly focused on either multilocus models that identify associations with a single trait or multitrait models that scan a single marker at a time. Since these types of models cannot fully utilize the association information, the powers of the tests are usually low. To potentially address this problem, we present here a multitrait multilocus (MTML) modeling framework that implements in three steps: (1) simplify the complex calculation; (2) reduce the model dimension; (3) integrate the joint contribution of single markers to multiple traits by Cauchy combination. The performances of MTML are evaluated and compared with other three published methods by Monte Carlo simulations. Simulation results show that MTML is more powerful for quantitative trait nucleotide detection and robust for various numbers of traits. In the meanwhile, MTML can effectively control type I error rate at a reasonable level. Real data analysis of <i>Arabidopsis thaliana</i> shows that MTML identifies more pleiotropic genetic associations. Therefore, we conclude that MTML is an efficient GWAS method for joint analysis of multiple quantitative traits. The R package MTML, which facilitates the implementation of the proposed method, is publicly available on GitHub https://github.com/Guohongping/MTML.</p>\u0000 </div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141794125","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

Factor-Analytic Variance–Covariance Structures for Prediction Into a Target Population of Environments 用于预测目标环境人群的因子分析方差-协方差结构。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-25 DOI: 10.1002/bimj.202400008

Hans-Peter Piepho, Emlyn Williams

引用次数: 0