Biometrical Journal最新文献_第9页

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":"66 6","pages":""},"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":"66 6","pages":""},"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":"66 6","pages":""},"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":"66 6","pages":""},"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

Issue Information: Biometrical Journal 6'24 发行信息：生物计量学杂志 6'24

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-22 DOI: 10.1002/bimj.202470006

引用次数: 0

A Marginalized Zero-Inflated Negative Binomial Model for Spatial Data: Modeling COVID-19 Deaths in Georgia 空间数据的边际零膨胀负二项模型：佐治亚州 COVID-19 死亡建模。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-13 DOI: 10.1002/bimj.202300182

Fedelis Mutiso, John L. Pearce, Sara E. Benjamin-Neelon, Noel T. Mueller, Hong Li, Brian Neelon

{"title":"A Marginalized Zero-Inflated Negative Binomial Model for Spatial Data: Modeling COVID-19 Deaths in Georgia","authors":"Fedelis Mutiso, John L. Pearce, Sara E. Benjamin-Neelon, Noel T. Mueller, Hong Li, Brian Neelon","doi":"10.1002/bimj.202300182","DOIUrl":"10.1002/bimj.202300182","url":null,"abstract":"<div>\u0000 \u0000 <p>Spatial count data with an abundance of zeros arise commonly in disease mapping studies. Typically, these data are analyzed using zero-inflated models, which comprise a mixture of a point mass at zero and an ordinary count distribution, such as the Poisson or negative binomial. However, due to their mixture representation, conventional zero-inflated models are challenging to explain in practice because the parameter estimates have conditional latent-class interpretations. As an alternative, several authors have proposed marginalized zero-inflated models that simultaneously model the excess zeros and the marginal mean, leading to a parameterization that more closely aligns with ordinary count models. Motivated by a study examining predictors of COVID-19 death rates, we develop a spatiotemporal marginalized zero-inflated negative binomial model that directly models the marginal mean, thus extending marginalized zero-inflated models to the spatial setting. To capture the spatiotemporal heterogeneity in the data, we introduce region-level covariates, smooth temporal effects, and spatially correlated random effects to model both the excess zeros and the marginal mean. For estimation, we adopt a Bayesian approach that combines full-conditional Gibbs sampling and Metropolis–Hastings steps. We investigate features of the model and use the model to identify key predictors of COVID-19 deaths in the US state of Georgia during the 2021 calendar year.</p></div>","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":"66 5","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141604513","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

Years of Life Lost to COVID-19 and Related Mortality Indicators: An Illustration in 30 Countries COVID-19 及相关死亡率指标造成的生命损失年数：30 个国家的说明。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-13 DOI: 10.1002/bimj.202300386

Valentin Rousson, Isabella Locatelli

引用次数: 0

Robust Regression Techniques for Multiple Method Comparison and Transformation 用于多种方法比较和转换的稳健回归技术。

IF 1.3 3区生物学

Biometrical Journal Pub Date : 2024-07-13 DOI: 10.1002/bimj.202400027

Florian Dufey

引用次数: 0