Biometrics最新文献_第10页

Discussion on "Bayesian meta-analysis of penetrance for cancer risk" by Thanthirige Lakshika M. Ruberu, Danielle Braun, Giovanni Parmigiani, and Swati Biswas. Thanthirige Lakshika M. Ruberu、Danielle Braun、Giovanni Parmigiani 和 Swati Biswas 关于 "癌症风险渗透的贝叶斯元分析 "的讨论。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae043

Moreno Ursino, Sarah Zohar

引用次数: 0

Direct and indirect treatment effects in the presence of semicompeting risks. 半竞争风险下的直接和间接治疗效果。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae032

Yuhao Deng, Yi Wang, Xiao-Hua Zhou

引用次数: 0

Rejoinder to the discussion on "Bayesian meta-analysis of penetrance for cancer risk". 对 "癌症风险渗透的贝叶斯元分析 "讨论的再评论。

IF 1.4 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae040

Thanthirige Lakshika M Ruberu, Danielle Braun, Giovanni Parmigiani, Swati Biswas

引用次数: 0

Differential recall bias in estimating treatment effects in observational studies. 在观察性研究中估计治疗效果时的不同回忆偏差。

IF 1.4 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae058

Suhwan Bong, Kwonsang Lee, Francesca Dominici

{"title":"Differential recall bias in estimating treatment effects in observational studies.","authors":"Suhwan Bong, Kwonsang Lee, Francesca Dominici","doi":"10.1093/biomtc/ujae058","DOIUrl":"10.1093/biomtc/ujae058","url":null,"abstract":"Observational studies are frequently used to estimate the effect of an exposure or treatment on an outcome. To obtain an unbiased estimate of the treatment effect, it is crucial to measure the exposure accurately. A common type of exposure misclassification is recall bias, which occurs in retrospective cohort studies when study subjects may inaccurately recall their past exposure. Particularly challenging is differential recall bias in the context of self-reported binary exposures, where the bias may be directional rather than random and its extent varies according to the outcomes experienced. This paper makes several contributions: (1) it establishes bounds for the average treatment effect even when a validation study is not available; (2) it proposes multiple estimation methods across various strategies predicated on different assumptions; and (3) it suggests a sensitivity analysis technique to assess the robustness of the causal conclusion, incorporating insights from prior research. The effectiveness of these methods is demonstrated through simulation studies that explore various model misspecification scenarios. These approaches are then applied to investigate the effect of childhood physical abuse on mental health in adulthood.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11199734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141449623","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

High-dimensional covariate-augmented overdispersed poisson factor model. 高维协变量增强过分散泊松因子模型。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae031

Wei Liu, Qingzhi Zhong

{"title":"High-dimensional covariate-augmented overdispersed poisson factor model.","authors":"Wei Liu, Qingzhi Zhong","doi":"10.1093/biomtc/ujae031","DOIUrl":"https://doi.org/10.1093/biomtc/ujae031","url":null,"abstract":"The current Poisson factor models often assume that the factors are unknown, which overlooks the explanatory potential of certain observable covariates. This study focuses on high dimensional settings, where the number of the count response variables and/or covariates can diverge as the sample size increases. A covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. A group of identifiability conditions is provided to theoretically guarantee computational identifiability. We incorporate the interdependence of both response variables and covariates by imposing a low-rank constraint on the large coefficient matrix. To address the computation challenges posed by nonlinearity, two high-dimensional latent matrices, and the low-rank constraint, we propose a novel variational estimation scheme that combines Laplace and Taylor approximations. We also develop a criterion based on a singular value ratio to determine the number of factors and the rank of the coefficient matrix. Comprehensive simulation studies demonstrate that the proposed method outperforms the state-of-the-art methods in estimation accuracy and computational efficiency. The practical merit of our method is demonstrated by an application to the CITE-seq dataset. A flexible implementation of our proposed method is available in the R package COAP.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140861837","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

Identifying temporal pathways using biomarkers in the presence of latent non-Gaussian components. 在存在潜在非高斯成分的情况下利用生物标记物识别时间路径。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae033

Shanghong Xie, Donglin Zeng, Yuanjia Wang

{"title":"Identifying temporal pathways using biomarkers in the presence of latent non-Gaussian components.","authors":"Shanghong Xie, Donglin Zeng, Yuanjia Wang","doi":"10.1093/biomtc/ujae033","DOIUrl":"https://doi.org/10.1093/biomtc/ujae033","url":null,"abstract":"Time-series data collected from a network of random variables are useful for identifying temporal pathways among the network nodes. Observed measurements may contain multiple sources of signals and noises, including Gaussian signals of interest and non-Gaussian noises, including artifacts, structured noise, and other unobserved factors (eg, genetic risk factors, disease susceptibility). Existing methods, including vector autoregression (VAR) and dynamic causal modeling do not account for unobserved non-Gaussian components. Furthermore, existing methods cannot effectively distinguish contemporaneous relationships from temporal relations. In this work, we propose a novel method to identify latent temporal pathways using time-series biomarker data collected from multiple subjects. The model adjusts for the non-Gaussian components and separates the temporal network from the contemporaneous network. Specifically, an independent component analysis (ICA) is used to extract the unobserved non-Gaussian components, and residuals are used to estimate the contemporaneous and temporal networks among the node variables based on method of moments. The algorithm is fast and can easily scale up. We derive the identifiability and the asymptotic properties of the temporal and contemporaneous networks. We demonstrate superior performance of our method by extensive simulations and an application to a study of attention-deficit/hyperactivity disorder (ADHD), where we analyze the temporal relationships between brain regional biomarkers. We find that temporal network edges were across different brain regions, while most contemporaneous network edges were bilateral between the same regions and belong to a subset of the functional connectivity network.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140847961","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

Rejoinder to "On exact randomization-based covariate-adjusted confidence intervals" by Jacob Fiksel. 对 Jacob Fiksel 所作 "基于精确随机化的协变量调整置信区间 "的反驳。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae052

Ke Zhu, Hanzhong Liu

引用次数: 0

Testing conditional quantile independence with functional covariate. 利用函数协变量测试条件量子独立性

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae036

Yongzhen Feng, Jie Li, Xiaojun Song

引用次数: 0

Topical hidden genome: discovering latent cancer mutational topics using a Bayesian multilevel context-learning approach. 主题隐藏基因组：利用贝叶斯多层次语境学习方法发现潜在的癌症突变主题。

IF 1.4 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae030

Saptarshi Chakraborty, Zoe Guan, Colin B Begg, Ronglai Shen

{"title":"Topical hidden genome: discovering latent cancer mutational topics using a Bayesian multilevel context-learning approach.","authors":"Saptarshi Chakraborty, Zoe Guan, Colin B Begg, Ronglai Shen","doi":"10.1093/biomtc/ujae030","DOIUrl":"10.1093/biomtc/ujae030","url":null,"abstract":"Inferring the cancer-type specificities of ultra-rare, genome-wide somatic mutations is an open problem. Traditional statistical methods cannot handle such data due to their ultra-high dimensionality and extreme data sparsity. To harness information in rare mutations, we have recently proposed a formal multilevel multilogistic \"hidden genome\" model. Through its hierarchical layers, the model condenses information in ultra-rare mutations through meta-features embodying mutation contexts to characterize cancer types. Consistent, scalable point estimation of the model can incorporate 10s of millions of variants across thousands of tumors and permit impressive prediction and attribution. However, principled statistical inference is infeasible due to the volume, correlation, and noninterpretability of mutation contexts. In this paper, we propose a novel framework that leverages topic models from computational linguistics to effectuate dimension reduction of mutation contexts producing interpretable, decorrelated meta-feature topics. We propose an efficient MCMC algorithm for implementation that permits rigorous full Bayesian inference at a scale that is orders of magnitude beyond the capability of existing out-of-the-box inferential high-dimensional multi-class regression methods and software. Applying our model to the Pan Cancer Analysis of Whole Genomes dataset reveals interesting biological insights including somatic mutational topics associated with UV exposure in skin cancer, aging in colorectal cancer, and strong influence of epigenome organization in liver cancer. Under cross-validation, our model demonstrates highly competitive predictive performance against blackbox methods of random forest and deep learning.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11056772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140847962","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

Integrating randomized and observational studies to estimate optimal dynamic treatment regimes. 整合随机研究和观察研究，估算最佳动态治疗方案。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae046

Anna Batorsky, Kevin J Anstrom, Donglin Zeng

{"title":"Integrating randomized and observational studies to estimate optimal dynamic treatment regimes.","authors":"Anna Batorsky, Kevin J Anstrom, Donglin Zeng","doi":"10.1093/biomtc/ujae046","DOIUrl":"10.1093/biomtc/ujae046","url":null,"abstract":"Sequential multiple assignment randomized trials (SMARTs) are the gold standard for estimating optimal dynamic treatment regimes (DTRs), but are costly and require a large sample size. We introduce the multi-stage augmented Q-learning estimator (MAQE) to improve efficiency of estimation of optimal DTRs by augmenting SMART data with observational data. Our motivating example comes from the Back Pain Consortium, where one of the overarching aims is to learn how to tailor treatments for chronic low back pain to individual patient phenotypes, knowledge which is lacking clinically. The Consortium-wide collaborative SMART and observational studies within the Consortium collect data on the same participant phenotypes, treatments, and outcomes at multiple time points, which can easily be integrated. Previously published single-stage augmentation methods for integration of trial and observational study (OS) data were adapted to estimate optimal DTRs from SMARTs using Q-learning. Simulation studies show the MAQE, which integrates phenotype, treatment, and outcome information from multiple studies over multiple time points, more accurately estimates the optimal DTR, and has a higher average value than a comparable Q-learning estimator without augmentation. We demonstrate this improvement is robust to a wide range of trial and OS sample sizes, addition of noise variables, and effect sizes.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11130757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141157534","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