IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae132

Xinyuan Tian, Fan Li, Li Shen, Denise Esserman, Yize Zhao

{"title":"Bayesian pathway analysis over brain network mediators for survival data.","authors":"Xinyuan Tian, Fan Li, Li Shen, Denise Esserman, Yize Zhao","doi":"10.1093/biomtc/ujae132","DOIUrl":"10.1093/biomtc/ujae132","url":null,"abstract":"Technological advancements in noninvasive imaging facilitate the construction of whole brain interconnected networks, known as brain connectivity. Existing approaches to analyze brain connectivity frequently disaggregate the entire network into a vector of unique edges or summary measures, leading to a substantial loss of information. Motivated by the need to explore the effect mechanism among genetic exposure, brain connectivity, and time to disease onset with maximum information extraction, we propose a Bayesian approach to model the effect pathway between each of these components while quantifying the mediating role of brain networks. To accommodate the biological architectures of brain connectivity constructed along white matter fiber tracts, we develop a structural model which includes a symmetric matrix-variate accelerated failure time model for disease onset and a symmetric matrix response regression for the network-variate mediator. We further impose within-graph sparsity and between-graph shrinkage to identify informative network configurations and eliminate the interference of noisy components. Simulations are carried out to confirm the advantages of our proposed method over existing alternatives. By applying the proposed method to the landmark Alzheimer's Disease Neuroimaging Initiative study, we obtain neurobiologically plausible insights that may inform future intervention strategies.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11555425/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142614070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic factor analysis with dependent Gaussian processes for high-dimensional gene expression trajectories. 针对高维基因表达轨迹的依存高斯过程动态因子分析。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae131

Jiachen Cai, Robert J B Goudie, Colin Starr, Brian D M Tom

{"title":"Dynamic factor analysis with dependent Gaussian processes for high-dimensional gene expression trajectories.","authors":"Jiachen Cai, Robert J B Goudie, Colin Starr, Brian D M Tom","doi":"10.1093/biomtc/ujae131","DOIUrl":"https://doi.org/10.1093/biomtc/ujae131","url":null,"abstract":"The increasing availability of high-dimensional, longitudinal measures of gene expression can facilitate understanding of biological mechanisms, as required for precision medicine. Biological knowledge suggests that it may be best to describe complex diseases at the level of underlying pathways, which may interact with one another. We propose a Bayesian approach that allows for characterizing such correlation among different pathways through dependent Gaussian processes (DGP) and mapping the observed high-dimensional gene expression trajectories into unobserved low-dimensional pathway expression trajectories via Bayesian sparse factor analysis. Our proposal is the first attempt to relax the classical assumption of independent factors for longitudinal data and has demonstrated a superior performance in recovering the shape of pathway expression trajectories, revealing the relationships between genes and pathways, and predicting gene expressions (closer point estimates and narrower predictive intervals), as demonstrated through simulations and real data analysis. To fit the model, we propose a Monte Carlo expectation maximization (MCEM) scheme that can be implemented conveniently by combining a standard Markov Chain Monte Carlo sampler and an R package GPFDA,which returns the maximum likelihood estimates of DGP hyperparameters. The modular structure of MCEM makes it generalizable to other complex models involving the DGP model component. Our R package DGP4LCF that implements the proposed approach is available on the Comprehensive R Archive Network (CRAN).","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142646915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust model averaging approach by Mallows-type criterion. 采用 Mallows 型标准的稳健模型平均法。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae128

Miaomiao Wang, Kang You, Lixing Zhu, Guohua Zou

{"title":"Robust model averaging approach by Mallows-type criterion.","authors":"Miaomiao Wang, Kang You, Lixing Zhu, Guohua Zou","doi":"10.1093/biomtc/ujae128","DOIUrl":"https://doi.org/10.1093/biomtc/ujae128","url":null,"abstract":"Model averaging is an important tool for treating uncertainty from model selection process and fusing information from different models, and has been widely used in various fields. However, the most existing model averaging criteria are proposed based on the methods of ordinary least squares or maximum likelihood, which possess high sensitivity to outliers or violation of certain model assumption. For the mean regression, no optimal robust methods are developed. To fill this gap, in our paper, we propose an outlier-robust model averaging approach by Mallows-type criterion. The idea is that we first construct a generalized M (GM) estimator for each candidate model, and then build robust weighting schemes by the asymptotic expansion of the final prediction error based on the GM-type loss function. So, we can still achieve a trustworthy result even if the dataset is contaminated by outliers in response and/or covariates. Asymptotic properties of the proposed robust model averaging estimators are established under some regularity conditions. The consistency of our weight estimators tending to the theoretically optimal weight vectors is also derived. We prove that our model averaging estimator is robust in terms of having bounded influence function. Further, we define the empirical prediction influence function to evaluate the quantitative robustness of the model averaging estimator. A simulation study and a real data analysis are conducted to demonstrate the finite sample performance of our estimators and compare them with other commonly used model selection and averaging methods.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142614075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SAM: Self-adapting mixture prior to dynamically borrow information from historical data in clinical trials SAM：在临床试验中从历史数据中动态借用信息之前的自适应混合物。

IF 1.9 4区数学

Biometrics Pub Date : 2023-09-18 DOI: 10.1111/biom.13927

Peng Yang, Yuansong Zhao, Lei Nie, Jonathon Vallejo, Ying Yuan

{"title":"SAM: Self-adapting mixture prior to dynamically borrow information from historical data in clinical trials","authors":"Peng Yang, Yuansong Zhao, Lei Nie, Jonathon Vallejo, Ying Yuan","doi":"10.1111/biom.13927","DOIUrl":"10.1111/biom.13927","url":null,"abstract":"Mixture priors provide an intuitive way to incorporate historical data while accounting for potential prior-data conflict by combining an informative prior with a noninformative prior. However, prespecifying the mixing weight for each component remains a crucial challenge. Ideally, the mixing weight should reflect the degree of prior-data conflict, which is often unknown beforehand, posing a significant obstacle to the application and acceptance of mixture priors. To address this challenge, we introduce self-adapting mixture (SAM) priors that determine the mixing weight using likelihood ratio test statistics or Bayes factors. SAM priors are data-driven and self-adapting, favoring the informative (noninformative) prior component when there is little (substantial) evidence of prior-data conflict. Consequently, SAM priors achieve dynamic information borrowing. We demonstrate that SAM priors exhibit desirable properties in both finite and large samples and achieve information-borrowing consistency. Moreover, SAM priors are easy to compute, data-driven, and calibration-free, mitigating the risk of data dredging. Numerical studies show that SAM priors outperform existing methods in adopting prior-data conflicts effectively. We developed R package “SAMprior” and web application that are freely available at CRAN and www.trialdesign.org to facilitate the use of SAM priors.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"79 4","pages":"2857-2868"},"PeriodicalIF":1.9,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10636192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Regression-based multiple treatment effect estimation under covariate-adaptive randomization 协变量适应性随机化下基于回归的多重治疗效果估计。

IF 1.9 4区数学

Biometrics Pub Date : 2023-09-12 DOI: 10.1111/biom.13925

Yujia Gu, Hanzhong Liu, Wei Ma

引用次数: 0

Latent factor model for multivariate functional data 多元函数数据的潜在因素模型。

IF 1.9 4区数学

Biometrics Pub Date : 2023-09-04 DOI: 10.1111/biom.13924

Ruonan Li, Luo Xiao

引用次数: 0

Functional Bayesian networks for discovering causality from multivariate functional data 从多元功能数据中发现因果关系的功能贝叶斯网络。

IF 1.9 4区数学

Biometrics Pub Date : 2023-08-28 DOI: 10.1111/biom.13922

Fangting Zhou, Kejun He, Kunbo Wang, Yanxun Xu, Yang Ni

引用次数: 0

Optimizing treatment allocation in randomized clinical trials by leveraging baseline covariates 利用基线协变量优化随机临床试验中的治疗分配。

IF 1.9 4区数学

Biometrics Pub Date : 2023-08-28 DOI: 10.1111/biom.13914

Wei Zhang, Zhiwei Zhang, Aiyi Liu

{"title":"Optimizing treatment allocation in randomized clinical trials by leveraging baseline covariates","authors":"Wei Zhang, Zhiwei Zhang, Aiyi Liu","doi":"10.1111/biom.13914","DOIUrl":"10.1111/biom.13914","url":null,"abstract":"We consider the problem of optimizing treatment allocation for statistical efficiency in randomized clinical trials. Optimal allocation has been studied previously for simple treatment effect estimators such as the sample mean difference, which are not fully efficient in the presence of baseline covariates. More efficient estimators can be obtained by incorporating covariate information, and modern machine learning methods make it increasingly feasible to approach full efficiency. Accordingly, we derive the optimal allocation ratio by maximizing the design efficiency of a randomized trial, assuming that an efficient estimator will be used for analysis. We then expand the scope of optimization by considering covariate-dependent randomization (CDR), which has some flavor of an observational study but provides the same level of scientific rigor as a standard randomized trial. We describe treatment effect estimators that are consistent, asymptotically normal, and (nearly) efficient under CDR, and derive the optimal propensity score by maximizing the design efficiency of a CDR trial (under the assumption that an efficient estimator will be used for analysis). Our optimality results translate into optimal designs that improve upon standard practice. Real-world examples and simulation results demonstrate that the proposed designs can produce substantial efficiency improvements in realistic settings.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"79 4","pages":"2815-2829"},"PeriodicalIF":1.9,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10166616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Study design for restricted mean time analysis of recurrent events and death 对复发和死亡事件进行限制性平均时间分析的研究设计。

IF 1.9 4区数学

Biometrics Pub Date : 2023-08-23 DOI: 10.1111/biom.13923

Lu Mao

{"title":"Study design for restricted mean time analysis of recurrent events and death","authors":"Lu Mao","doi":"10.1111/biom.13923","DOIUrl":"10.1111/biom.13923","url":null,"abstract":"The restricted mean time in favor (RMT-IF) of treatment has just been added to the analytic toolbox for composite endpoints of recurrent events and death. To help practitioners design new trials based on this method, we develop tools to calculate the sample size and power. Specifically, we formulate the outcomes as a multistate Markov process with a sequence of transient states for recurrent events and an absorbing state for death. The transition intensities, in this case the instantaneous risks of another nonfatal event or death, are assumed to be time-homogeneous but nonetheless allowed to depend on the number of past events. Using the properties of Coxian distributions, we derive the RMT-IF effect size under the alternative hypothesis as a function of the treatment-to-control intensity ratios along with the baseline intensities, the latter of which can be easily estimated from historical data. We also reduce the variance of the nonparametric RMT-IF estimator to calculable terms under a standard set-up for censoring. Simulation studies show that the resulting formulas provide accurate approximation to the sample size and power in realistic settings. For illustration, a past cardiovascular trial with recurrent-hospitalization and mortality outcomes is analyzed to generate the parameters needed to design a future trial. The procedures are incorporated into the rmt package along with the original methodology on the Comprehensive R Archive Network (CRAN).","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"79 4","pages":"3701-3714"},"PeriodicalIF":1.9,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/biom.13923","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10060354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generating designs for comparative experiments with two blocking factors 生成具有两个阻断因子的比较实验设计。

IF 1.9 4区数学

Biometrics Pub Date : 2023-08-18 DOI: 10.1111/biom.13913

Nha Vo-Thanh, Hans-Peter Piepho

{"title":"Generating designs for comparative experiments with two blocking factors","authors":"Nha Vo-Thanh, Hans-Peter Piepho","doi":"10.1111/biom.13913","DOIUrl":"10.1111/biom.13913","url":null,"abstract":"Often, comparative experiments involve a single treatment factor and two blocking factors, for example, augmented row–column, two-phase, and incomplete row–column experiments. These experiments are widely used in agriculture. Finding good designs for these experiments is a major challenge when the number of treatments is large and the blocking structure is complex. In this paper, we first propose a new search algorithm that is combined with efficient update formulae, so that optimal designs with two blocking factors can be found within a reasonable time. Second, we compare augmented row–column designs generated with our new method to those obtained from CycDesigN, DiGGer, and the OPTEX procedure of SAS in terms of computing times as well as the quality of solutions. Third, we illustrate our proposed approach with four applications. We show an example where our efficient update formulae work while existing update formulae cannot be applied, and we use our search framework to generate augmented row–column, two-phase, and incomplete row–column designs. We end the paper with a conclusion along with suggestions for potential applications.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"79 4","pages":"3574-3585"},"PeriodicalIF":1.9,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/biom.13913","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10022135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Biometrics最新文献