Biometrical Journal最新文献_第8页

A generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints 针对多终点篮子试验的广义校准贝叶斯分层建模方法。

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-02-17 DOI: 10.1002/bimj.202300122

Xiaohan Chi, Ying Yuan, Zhangsheng Yu, Ruitao Lin

{"title":"A generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints","authors":"Xiaohan Chi, Ying Yuan, Zhangsheng Yu, Ruitao Lin","doi":"10.1002/bimj.202300122","DOIUrl":"10.1002/bimj.202300122","url":null,"abstract":"A basket trial simultaneously evaluates a treatment in multiple cancer subtypes, offering an effective way to accelerate drug development in multiple indications. Many basket trials are designed and monitored based on a single efficacy endpoint, primarily the tumor response. For molecular targeted or immunotherapy agents, however, a single efficacy endpoint cannot adequately characterize the treatment effect. It is increasingly important to use more complex endpoints to comprehensively assess the risk–benefit profile of such targeted therapies. We extend the calibrated Bayesian hierarchical modeling approach to monitor phase II basket trials with multiple endpoints. We propose two generalizations, one based on the latent variable approach and the other based on the multinomial–normal hierarchical model, to accommodate different types of endpoints and dependence assumptions regarding information sharing. We introduce shrinkage parameters as functions of statistics measuring homogeneity among subgroups and propose a general calibration approach to determine the functional forms. Theoretical properties of the generalized hierarchical models are investigated. Simulation studies demonstrate that the monitoring procedure based on the generalized approach yields desirable operating characteristics.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139898345","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

Sparse multiway canonical correlation analysis for multimodal stroke recovery data 多模态中风恢复数据的稀疏多向典型相关分析

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-02-17 DOI: 10.1002/bimj.202300037

Subham Das, Franklin D. West, Cheolwoo Park

引用次数: 0

Estimating the proportion of true null hypotheses and adaptive false discovery rate control in discrete paradigm 离散范式中真实无效假设比例的估计和自适应错误发现率控制。

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-02-14 DOI: 10.1002/bimj.202200204

Aniket Biswas, Gaurangadeb Chattopadhyay

引用次数: 0

A review on statistical and machine learning competing risks methods 统计和机器学习竞争风险方法综述。

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-02-13 DOI: 10.1002/bimj.202300060

Karla Monterrubio-Gómez, Nathan Constantine-Cooke, Catalina A. Vallejos

引用次数: 0

Editorial Board: Biometrical Journal 2'24 编辑委员会：《生物计量学杂志》2'24

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-02-02 DOI: 10.1002/bimj.202470002

引用次数: 0

A Bayesian hierarchical approach to account for evidence and uncertainty in the modeling of infectious diseases: An application to COVID-19 在传染病建模中考虑证据和不确定性的贝叶斯分层方法：应用于 COVID-19。

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-01-28 DOI: 10.1002/bimj.202200341

Raphael Rehms, Nicole Ellenbach, Eva Rehfuess, Jacob Burns, Ulrich Mansmann, Sabine Hoffmann

{"title":"A Bayesian hierarchical approach to account for evidence and uncertainty in the modeling of infectious diseases: An application to COVID-19","authors":"Raphael Rehms, Nicole Ellenbach, Eva Rehfuess, Jacob Burns, Ulrich Mansmann, Sabine Hoffmann","doi":"10.1002/bimj.202200341","DOIUrl":"10.1002/bimj.202200341","url":null,"abstract":"Infectious disease models can serve as critical tools to predict the development of cases and associated healthcare demand and to determine the set of nonpharmaceutical interventions (NPIs) that is most effective in slowing the spread of an infectious agent. Current approaches to estimate NPI effects typically focus on relatively short time periods and either on the number of reported cases, deaths, intensive care occupancy, or hospital occupancy as a single indicator of disease transmission. In this work, we propose a Bayesian hierarchical model that integrates multiple outcomes and complementary sources of information in the estimation of the true and unknown number of infections while accounting for time-varying underreporting and weekday-specific delays in reported cases and deaths, allowing us to estimate the number of infections on a daily basis rather than having to smooth the data. To address dynamic changes occurring over long periods of time, we account for the spread of new variants, seasonality, and time-varying differences in host susceptibility. We implement a Markov chain Monte Carlo algorithm to conduct Bayesian inference and illustrate the proposed approach with data on COVID-19 from 20 European countries. The approach shows good performance on simulated data and produces posterior predictions that show a good fit to reported cases, deaths, hospital, and intensive care occupancy.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202200341","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572218","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

A comparison of strategies for selecting auxiliary variables for multiple imputation 多重估算辅助变量选择策略比较

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-01-23 DOI: 10.1002/bimj.202200291

Rheanna M. Mainzer, Cattram D. Nguyen, John B. Carlin, Margarita Moreno-Betancur, Ian R. White, Katherine J. Lee

{"title":"A comparison of strategies for selecting auxiliary variables for multiple imputation","authors":"Rheanna M. Mainzer, Cattram D. Nguyen, John B. Carlin, Margarita Moreno-Betancur, Ian R. White, Katherine J. Lee","doi":"10.1002/bimj.202200291","DOIUrl":"https://doi.org/10.1002/bimj.202200291","url":null,"abstract":"Multiple imputation (MI) is a popular method for handling missing data. Auxiliary variables can be added to the imputation model(s) to improve MI estimates. However, the choice of which auxiliary variables to include is not always straightforward. Several data-driven auxiliary variable selection strategies have been proposed, but there has been limited evaluation of their performance. Using a simulation study we evaluated the performance of eight auxiliary variable selection strategies: (1, 2) two versions of selection based on correlations in the observed data; (3) selection using hypothesis tests of the “missing completely at random” assumption; (4) replacing auxiliary variables with their principal components; (5, 6) forward and forward stepwise selection; (7) forward selection based on the estimated fraction of missing information; and (8) selection via the least absolute shrinkage and selection operator (LASSO). A complete case analysis and an MI analysis using all auxiliary variables (the “full model”) were included for comparison. We also applied all strategies to a motivating case study. The full model outperformed all auxiliary variable selection strategies in the simulation study, with the LASSO strategy the best performing auxiliary variable selection strategy overall. All MI analysis strategies that we were able to apply to the case study led to similar estimates, although computational time was substantially reduced when variable selection was employed. This study provides further support for adopting an inclusive auxiliary variable strategy where possible. Auxiliary variable selection using the LASSO may be a promising alternative when the full model fails or is too burdensome.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202200291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139550466","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

Parametric modal regression with error in covariates 带有协变量误差的参数模态回归

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-01-19 DOI: 10.1002/bimj.202200348

Qingyang Liu, Xianzheng Huang

引用次数: 0

Finite mixtures in capture–recapture surveys for modeling residency patterns in marine wildlife populations 用于模拟海洋野生动物种群居住模式的捕获-再捕获调查有限混合物

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-01-16 DOI: 10.1002/bimj.202200350

Gianmarco Caruso, Pierfrancesco Alaimo Di Loro, Marco Mingione, Luca Tardella, Daniela Silvia Pace, Giovanna Jona Lasinio

{"title":"Finite mixtures in capture–recapture surveys for modeling residency patterns in marine wildlife populations","authors":"Gianmarco Caruso, Pierfrancesco Alaimo Di Loro, Marco Mingione, Luca Tardella, Daniela Silvia Pace, Giovanna Jona Lasinio","doi":"10.1002/bimj.202200350","DOIUrl":"https://doi.org/10.1002/bimj.202200350","url":null,"abstract":"This work aims to show how prior knowledge about the structure of a heterogeneous animal population can be leveraged to improve the abundance estimation from capture–recapture survey data. We combine the Open Jolly-Seber model with finite mixtures and propose a parsimonious specification tailored to the residency patterns of the common bottlenose dolphin. We employ a Bayesian framework for our inference, discussing the appropriate choice of priors to mitigate label-switching and nonidentifiability issues, commonly associated with finite mixture models. We conduct a series of simulation experiments to illustrate the competitive advantage of our proposal over less specific alternatives. The proposed approach is applied to data collected on the common bottlenose dolphin population inhabiting the Tiber River estuary (Mediterranean Sea). Our results provide novel insights into this population's size and structure, shedding light on some of the ecological processes governing its dynamics.","PeriodicalId":55360,"journal":{"name":"Biometrical Journal","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/bimj.202200350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139473964","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

Neutralise: An open science initiative for neutral comparison of two-sample tests 中和：对双样本测试进行中性比较的开放科学倡议

IF 1.7 3区生物学

Biometrical Journal Pub Date : 2024-01-12 DOI: 10.1002/bimj.202200237

Leyla Kodalci, Olivier Thas

引用次数: 0