Biometrics最新文献_第4页

Cumulative link mixed-effects models in the service of remote sensing crop progress monitoring.

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae137

Ioannis Oikonomidis, Samis Trevezas

{"title":"Cumulative link mixed-effects models in the service of remote sensing crop progress monitoring.","authors":"Ioannis Oikonomidis, Samis Trevezas","doi":"10.1093/biomtc/ujae137","DOIUrl":"https://doi.org/10.1093/biomtc/ujae137","url":null,"abstract":"This study introduces an innovative cumulative link modeling (CLM) approach to monitor crop progress over large areas using remote sensing data. Two distinct models are developed, a fixed-effects CLM and a mixed-effects one that incorporates annual random effects to capture the inherent inter-seasonal variability. Inference is based on partial-likelihood with two law variations, the standard CLM based on the multinomial distribution and a novel one based on the product binomial distribution. Model performance is evaluated on eight crops, namely corn, oats, sorghum, soybeans, winter wheat, alfalfa, dry beans, and millet, using in-situ data from Nebraska, USA, spanning 20 years. The models utilize the predictive attributes of calendar time, thermal time, and the normalized difference vegetation index. The results demonstrate the wide applicability of this approach to different crops, providing large-scale predictions of crop progress and allowing the estimation of important agronomic parameters. To facilitate reproducibility, an ecosystem of R packages has been developed and made publicly accessible under the name Ages of Man. The packages can be utilized to implement the presented methodology in any area with this type of data, including the USA.","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":"142827274","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

Unlocking the power of multi-institutional data: Integrating and harmonizing genomic data across institutions.

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae146

Yuan Chen, Ronglai Shen, Xiwen Feng, Katherine Panageas

{"title":"Unlocking the power of multi-institutional data: Integrating and harmonizing genomic data across institutions.","authors":"Yuan Chen, Ronglai Shen, Xiwen Feng, Katherine Panageas","doi":"10.1093/biomtc/ujae146","DOIUrl":"10.1093/biomtc/ujae146","url":null,"abstract":"Cancer is a complex disease driven by genomic alterations, and tumor sequencing is becoming a mainstay of clinical care for cancer patients. The emergence of multi-institution sequencing data presents a powerful resource for learning real-world evidence to enhance precision oncology. GENIE BPC, led by American Association for Cancer Research, establishes a unique database linking genomic data with clinical information for patients treated at multiple cancer centers. However, leveraging sequencing data from multiple institutions presents significant challenges. Variability in gene panels can lead to loss of information when analyses focus on genes common across panels. Additionally, differences in sequencing techniques and patient heterogeneity across institutions add complexity. High data dimensionality, sparse gene mutation patterns, and weak signals at the individual gene level further complicate matters. Motivated by these real-world challenges, we introduce the Bridge model. It uses a quantile-matched latent variable approach to derive integrated features to preserve information beyond common genes and maximize the utilization of all available data, while leveraging information sharing to enhance both learning efficiency and the model's capacity to generalize. By extracting harmonized and noise-reduced lower-dimensional latent variables, the true mutation pattern unique to each individual is captured. We assess model's performance and parameter estimation through extensive simulation studies. The extracted latent features from the Bridge model consistently excel in predicting patient survival across six cancer types in GENIE BPC data.","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":"142827091","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

Semiparametric sensitivity analysis: unmeasured confounding in observational studies. 半参数敏感性分析：观察性研究中的未测量混杂因素。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae106

Razieh Nabi, Matteo Bonvini, Edward H Kennedy, Ming-Yueh Huang, Marcela Smid, Daniel O Scharfstein

{"title":"Semiparametric sensitivity analysis: unmeasured confounding in observational studies.","authors":"Razieh Nabi, Matteo Bonvini, Edward H Kennedy, Ming-Yueh Huang, Marcela Smid, Daniel O Scharfstein","doi":"10.1093/biomtc/ujae106","DOIUrl":"https://doi.org/10.1093/biomtc/ujae106","url":null,"abstract":"Establishing cause-effect relationships from observational data often relies on untestable assumptions. It is crucial to know whether, and to what extent, the conclusions drawn from non-experimental studies are robust to potential unmeasured confounding. In this paper, we focus on the average causal effect (ACE) as our target of inference. We generalize the sensitivity analysis approach developed by Robins et al., Franks et al., and Zhou and Yao. We use semiparametric theory to derive the non-parametric efficient influence function of the ACE, for fixed sensitivity parameters. We use this influence function to construct a one-step, split sample, truncated estimator of the ACE. Our estimator depends on semiparametric models for the distribution of the observed data; importantly, these models do not impose any restrictions on the values of sensitivity analysis parameters. We establish sufficient conditions ensuring that our estimator has $sqrt{n}$ asymptotics. We use our methodology to evaluate the causal effect of smoking during pregnancy on birth weight. We also evaluate the performance of estimation procedure in a simulation study.","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":"142457176","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

Sensitivity analysis for studies transporting prediction models. 研究运输预测模型的敏感性分析。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae129

Jon A Steingrimsson, Sarah E Robertson, Sarah Voter, Issa J Dahabreh

{"title":"Sensitivity analysis for studies transporting prediction models.","authors":"Jon A Steingrimsson, Sarah E Robertson, Sarah Voter, Issa J Dahabreh","doi":"10.1093/biomtc/ujae129","DOIUrl":"10.1093/biomtc/ujae129","url":null,"abstract":"We consider estimation of measures of model performance in a target population when covariate and outcome data are available from a source population and covariate data, but not outcome data, are available from the target population. In this setting, identification of measures of model performance is possible under an untestable assumption that the outcome and population (source or target) are independent conditional on covariates. In practice, this assumption is uncertain and, in some cases, controversial. Therefore, sensitivity analysis may be useful for examining the impact of assumption violations on inferences about model performance. Here, we propose an exponential tilt sensitivity analysis model and develop statistical methods to determine how measures of model performance are affected by violations of the assumption of conditional independence between outcome and population. We provide identification results and estimators for the risk in the target population under the sensitivity analysis model, examine the large-sample properties of the estimators, and apply them to data on lung cancer screening.","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/PMC11582396/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685904","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

Bayesian network-guided sparse regression with flexible varying effects. 具有灵活变化效应的贝叶斯网络引导稀疏回归。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae111

Yangfan Ren, Christine B Peterson, Marina Vannucci

{"title":"Bayesian network-guided sparse regression with flexible varying effects.","authors":"Yangfan Ren, Christine B Peterson, Marina Vannucci","doi":"10.1093/biomtc/ujae111","DOIUrl":"https://doi.org/10.1093/biomtc/ujae111","url":null,"abstract":"In this paper, we propose Varying Effects Regression with Graph Estimation (VERGE), a novel Bayesian method for feature selection in regression. Our model has key aspects that allow it to leverage the complex structure of data sets arising from genomics or imaging studies. We distinguish between the predictors, which are the features utilized in the outcome prediction model, and the subject-level covariates, which modulate the effects of the predictors on the outcome. We construct a varying coefficients modeling framework where we infer a network among the predictor variables and utilize this network information to encourage the selection of related predictors. We employ variable selection spike-and-slab priors that enable the selection of both network-linked predictor variables and covariates that modify the predictor effects. We demonstrate through simulation studies that our method outperforms existing alternative methods in terms of both feature selection and predictive accuracy. We illustrate VERGE with an application to characterizing the influence of gut microbiome features on obesity, where we identify a set of microbial taxa and their ecological dependence relations. We allow subject-level covariates, including sex and dietary intake variables to modify the coefficients of the microbiome predictors, providing additional insight into the interplay between these factors.","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":"142387634","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

Bayesian pathway analysis over brain network mediators for survival data. 针对生存数据的脑网络介质贝叶斯路径分析

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

Graphical model inference with external network data.

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae151

Jack Jewson, Li Li, Laura Battaglia, Stephen Hansen, David Rossell, Piotr Zwiernik

引用次数: 0

Optimal adaptive SMART designs with binary outcomes.

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae140

Rik Ghosh, Bibhas Chakraborty, Inbal Nahum-Shani, Megan E Patrick, Palash Ghosh

{"title":"Optimal adaptive SMART designs with binary outcomes.","authors":"Rik Ghosh, Bibhas Chakraborty, Inbal Nahum-Shani, Megan E Patrick, Palash Ghosh","doi":"10.1093/biomtc/ujae140","DOIUrl":"https://doi.org/10.1093/biomtc/ujae140","url":null,"abstract":"In a sequential multiple-assignment randomized trial (SMART), a sequence of treatments is given to a patient over multiple stages. In each stage, randomization may be done to allocate patients to different treatment groups. Even though SMART designs are getting popular among clinical researchers, the methodologies for adaptive randomization at different stages of a SMART are few and not sophisticated enough to handle the complexity of optimal allocation of treatments at every stage of a trial. Lack of optimal allocation methodologies can raise critical concerns about SMART designs from an ethical point of view. In this work, we develop an optimal adaptive allocation procedure using a constrained optimization that minimizes the total expected number of treatment failures for a SMART with a binary primary outcome, subject to a fixed asymptotic variance of a predefined objective function. Issues related to optimal adaptive allocations are explored theoretically with supporting simulations. The applicability of the proposed methodology is demonstrated using a recently conducted SMART study named M-bridge for developing universal and resource-efficient dynamic treatment regimes for incoming first-year college students as a bridge to desirable treatments to address alcohol-related risks.","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":"142827001","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

Joint mirror procedure: controlling false discovery rate for identifying simultaneous signals.

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae142

Linsui Deng, Kejun He, Xianyang Zhang

{"title":"Joint mirror procedure: controlling false discovery rate for identifying simultaneous signals.","authors":"Linsui Deng, Kejun He, Xianyang Zhang","doi":"10.1093/biomtc/ujae142","DOIUrl":"10.1093/biomtc/ujae142","url":null,"abstract":"In many applications, the process of identifying a specific feature of interest often involves testing multiple hypotheses for their joint statistical significance. Examples include mediation analysis, which simultaneously examines the existence of the exposure-mediator and the mediator-outcome effects, and replicability analysis, aiming to identify simultaneous signals that exhibit statistical significance across multiple independent studies. In this work, we present a new approach called the joint mirror (JM) procedure that effectively detects such features while maintaining false discovery rate (FDR) control in finite samples. The JM procedure employs an iterative method that gradually shrinks the rejection region based on progressively revealed information until a conservative estimate of the false discovery proportion is below the target FDR level. Additionally, we introduce a more stringent error measure known as the composite FDR (cFDR), which assigns weights to each false discovery based on its number of null components. We use the leave-one-out technique to prove that the JM procedure controls the cFDR in finite samples. To implement the JM procedure, we propose an efficient algorithm that can incorporate partial ordering information. Through extensive simulations, we show that our procedure effectively controls the cFDR and enhances statistical power across various scenarios, including the case that test statistics are dependent across the features. Finally, we showcase the utility of our method by applying it to real-world mediation and replicability analyses.","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/PMC11639532/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821778","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