Biometrics最新文献_第2页

Jointly modeling means and variances for nonlinear mixed effects models with measurement errors and outliers. 具有测量误差和异常值的非线性混合效应模型的均值和方差联合建模。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf018

Qian Ye, Lang Wu, Viviane Dias Lima

引用次数: 0

Robust Bayesian graphical regression models for assessing tumor heterogeneity in proteomic networks. 评估蛋白质组学网络中肿瘤异质性的稳健贝叶斯图形回归模型。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae160

Tsung-Hung Yao, Yang Ni, Anindya Bhadra, Jian Kang, Veerabhadran Baladandayuthapani

{"title":"Robust Bayesian graphical regression models for assessing tumor heterogeneity in proteomic networks.","authors":"Tsung-Hung Yao, Yang Ni, Anindya Bhadra, Jian Kang, Veerabhadran Baladandayuthapani","doi":"10.1093/biomtc/ujae160","DOIUrl":"https://doi.org/10.1093/biomtc/ujae160","url":null,"abstract":"Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of two canonical assumptions: (i) a homogeneous graph with a common network for all subjects or (ii) an assumption of normality, especially in the context of Gaussian graphical models. Both assumptions are restrictive and can fail to hold in certain applications such as proteomic networks in cancer. To this end, we propose an approach termed robust Bayesian graphical regression (rBGR) to estimate heterogeneous graphs for non-normally distributed data. rBGR is a flexible framework that accommodates non-normality through random marginal transformations and constructs covariate-dependent graphs to accommodate heterogeneity through graphical regression techniques. We formulate a new characterization of edge dependencies in such models called conditional sign independence with covariates, along with an efficient posterior sampling algorithm. In simulation studies, we demonstrate that rBGR outperforms existing graphical regression models for data generated under various levels of non-normality in both edge and covariate selection. We use rBGR to assess proteomic networks in lung and ovarian cancers to systematically investigate the effects of immunogenic heterogeneity within tumors. Our analyses reveal several important protein-protein interactions that are differentially associated with the immune cell abundance; some corroborate existing biological knowledge, whereas others are novel findings.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969463","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

A general, flexible, and harmonious framework to construct interpretable functions in regression analysis. 在回归分析中构造可解释函数的一个通用的、灵活的、和谐的框架。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf014

Tianyu Zhan, Jian Kang

{"title":"A general, flexible, and harmonious framework to construct interpretable functions in regression analysis.","authors":"Tianyu Zhan, Jian Kang","doi":"10.1093/biomtc/ujaf014","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf014","url":null,"abstract":"An interpretable model or method has several appealing features, such as reliability to adversarial examples, transparency of decision-making, and communication facilitator. However, interpretability is a subjective concept, and even its definition can be diverse. The same model may be deemed as interpretable by a study team, but regarded as a black-box algorithm by another squad. Simplicity, accuracy and generalizability are some additional important aspects of evaluating interpretability. In this work, we present a general, flexible and harmonious framework to construct interpretable functions in regression analysis with a focus on continuous outcomes. We formulate a functional skeleton in light of users' expectations of interpretability. A new measure based on Mallows's $C_p$-statistic is proposed for model selection to balance approximation, generalizability, and interpretability. We apply this approach to derive a sample size formula in adaptive clinical trial designs to demonstrate the general workflow, and to explain operating characteristics in a Bayesian Go/No-Go paradigm to show the potential advantages of using meaningful intermediate variables. Generalization to categorical outcomes is illustrated in an example of hypothesis testing based on Fisher's exact test. A real data analysis of NHANES (National Health and Nutrition Examination Survey) is conducted to investigate relationships between some important laboratory measurements. We also discuss some extensions of this method.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143555802","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

Optimal treatment regime estimation in practice: challenges and choices in a randomized clinical trial for depression. 实践中的最佳治疗方案评估：抑郁症随机临床试验的挑战和选择。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf026

Florian Stijven, Trung Dung Tran, Ellen Driessen, Ariel Alonso Abad, Geert Molenberghs, Geert Verbeke, Iven Van Mechelen

{"title":"Optimal treatment regime estimation in practice: challenges and choices in a randomized clinical trial for depression.","authors":"Florian Stijven, Trung Dung Tran, Ellen Driessen, Ariel Alonso Abad, Geert Molenberghs, Geert Verbeke, Iven Van Mechelen","doi":"10.1093/biomtc/ujaf026","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf026","url":null,"abstract":"An important aspect of precision medicine is the tailoring of treatments to specific patient types. Nowadays, various methods are available to estimate for this purpose so-called optimal treatment regimes, that is, decision rules for treatment assignment that map patterns of pretreatment characteristics to treatment alternatives and that maximize the expected patient benefit. However, the application of these methods to real-life data has been limited and comes with nonstandard statistical issues. In search of best practices, we reanalyzed data from a randomized clinical trial for the treatment of dysthymic disorder. While the original objective of this trial was to detect a marginally best treatment alternative, we wanted to estimate an optimal treatment regime using 2 prominent estimation methods: Q-learning and value search estimation. An important obstacle in the dataset under study was the occurrence of missing values. This was handled with multiple imputation, a thoughtful implementation of which, however, implied several challenges. Other challenges were implied by the concrete implementation of value search estimation. In this paper, all the choices we have made in the analysis to handle the aforementioned issues are detailed together with a motivation and a description of possible alternatives. Accordingly, this paper may serve as a guide to apply optimal treatment regime estimation in data-analytic practice.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661937","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

Composite likelihood inference for space-time point processes. 时空点过程的复合似然推理。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf009

Abdollah Jalilian, Francisco Cuevas-Pacheco, Ganggang Xu, Rasmus Waagepetersen

{"title":"Composite likelihood inference for space-time point processes.","authors":"Abdollah Jalilian, Francisco Cuevas-Pacheco, Ganggang Xu, Rasmus Waagepetersen","doi":"10.1093/biomtc/ujaf009","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf009","url":null,"abstract":"The dynamics of a rain forest is extremely complex involving births, deaths, and growth of trees with complex interactions between trees, animals, climate, and environment. We consider the patterns of recruits (new trees) and dead trees between rain forest censuses. For a current census, we specify regression models for the conditional intensity of recruits and the conditional probabilities of death given the current trees and spatial covariates. We estimate regression parameters using conditional composite likelihood functions that only involve the conditional first order properties of the data. When constructing assumption lean estimators of covariance matrices of parameter estimates, we only need mild assumptions of decaying conditional correlations in space, while assumptions regarding correlations over time are avoided by exploiting conditional centering of composite likelihood score functions. Time series of point patterns from rain forest censuses are quite short, while each point pattern covers a fairly big spatial region. To obtain asymptotic results, we therefore use a central limit theorem for the fixed timespan-increasing spatial domain asymptotic setting. This also allows us to handle the challenge of using stochastic covariates constructed from past point patterns. Conveniently, it suffices to impose weak dependence assumptions on the innovations of the space-time process. We investigate the proposed methodology by simulation studies and an application to rain forest data.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143405365","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

Report of the Editors-2024. 编辑报告-2024。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf004

引用次数: 0

Pseudo-observations for bivariate survival data.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf006

Yael Travis-Lumer, Micha Mandel, Rebecca A Betensky

{"title":"Pseudo-observations for bivariate survival data.","authors":"Yael Travis-Lumer, Micha Mandel, Rebecca A Betensky","doi":"10.1093/biomtc/ujaf006","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf006","url":null,"abstract":"The pseudo-observations approach has been gaining popularity as a method to estimate covariate effects on censored survival data. It is used regularly to estimate covariate effects on quantities such as survival probabilities, restricted mean life, cumulative incidence, and others. In this work, we propose to generalize the pseudo-observations approach to situations where a bivariate failure-time variable is observed, subject to right censoring. The idea is to first estimate the joint survival function of both failure times and then use it to define the relevant pseudo-observations. Once the pseudo-observations are calculated, they are used as the response in a generalized linear model. We consider 2 common nonparametric estimators of the joint survival function: the estimator of Lin and Ying (1993) and the Dabrowska estimator (Dabrowska, 1988). For both estimators, we show that our bivariate pseudo-observations approach produces regression estimates that are consistent and asymptotically normal. Our proposed method enables estimation of covariate effects on quantities such as the joint survival probability at a fixed bivariate time point or simultaneously at several time points and, consequentially, can estimate covariate-adjusted conditional survival probabilities. We demonstrate the method using simulations and an analysis of 2 real-world datasets.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143188046","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

Gaussian processes for time series with lead-lag effects with applications to biology data. 超前滞后效应时间序列的高斯过程及其在生物数据中的应用。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae156

Wancen Mu, Jiawen Chen, Eric S Davis, Kathleen Reed, Douglas Phanstiel, Michael I Love, Didong Li

{"title":"Gaussian processes for time series with lead-lag effects with applications to biology data.","authors":"Wancen Mu, Jiawen Chen, Eric S Davis, Kathleen Reed, Douglas Phanstiel, Michael I Love, Didong Li","doi":"10.1093/biomtc/ujae156","DOIUrl":"10.1093/biomtc/ujae156","url":null,"abstract":"Investigating the relationship, particularly the lead-lag effect, between time series is a common question across various disciplines, especially when uncovering biological processes. However, analyzing time series presents several challenges. Firstly, due to technical reasons, the time points at which observations are made are not at uniform intervals. Secondly, some lead-lag effects are transient, necessitating time-lag estimation based on a limited number of time points. Thirdly, external factors also impact these time series, requiring a similarity metric to assess the lead-lag relationship. To counter these issues, we introduce a model grounded in the Gaussian process, affording the flexibility to estimate lead-lag effects for irregular time series. In addition, our method outputs dissimilarity scores, thereby broadening its applications to include tasks such as ranking or clustering multiple pairwise time series when considering their strength of lead-lag effects with external factors. Crucially, we offer a series of theoretical proofs to substantiate the validity of our proposed kernels and the identifiability of kernel parameters. Our model demonstrates advances in various simulations and real-world applications, particularly in the study of dynamic chromatin interactions, compared to other leading methods.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142943771","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

Distributed lag models for retrospective cohort data with application to a study of built environment and body weight.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae166

Jennifer F Bobb, Stephen J Mooney, Maricela Cruz, Anne Vernez Moudon, Adam Drewnowski, David Arterburn, Andrea J Cook

{"title":"Distributed lag models for retrospective cohort data with application to a study of built environment and body weight.","authors":"Jennifer F Bobb, Stephen J Mooney, Maricela Cruz, Anne Vernez Moudon, Adam Drewnowski, David Arterburn, Andrea J Cook","doi":"10.1093/biomtc/ujae166","DOIUrl":"10.1093/biomtc/ujae166","url":null,"abstract":"Distributed lag models (DLMs) estimate the health effects of exposure over multiple time lags prior to the outcome and are widely used in time series studies. Applying DLMs to retrospective cohort studies is challenging due to inconsistent lengths of exposure history across participants, which is common when using electronic health record databases. A standard approach is to define subcohorts of individuals with some minimum exposure history, but this limits power and may amplify selection bias. We propose alternative full-cohort methods that use all available data while simultaneously enabling examination of the longest time lag estimable in the cohort. Through simulation studies, we find that restricting to a subcohort can lead to biased estimates of exposure effects due to confounding by correlated exposures at more distant lags. By contrast, full-cohort methods that incorporate multiple imputation of complete exposure histories can avoid this bias to efficiently estimate lagged and cumulative effects. Applying full-cohort DLMs to a study examining the association between residential density (a proxy for walkability) over 12 years and body weight, we find evidence of an immediate effect in the prior 1-2 years. We also observed an association at the maximal lag considered (12 years prior), which we posit reflects an earlier ($ge$12 years) or incrementally increasing prior effect over time. DLMs can be efficiently incorporated within retrospective cohort studies to identify critical windows of exposure.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11760659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143031922","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

A mixed-effects Bayesian regression model for multivariate group testing data. 多变量组检验数据的混合效应贝叶斯回归模型。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf028

Christopher S McMahan, Chase N Joyner, Joshua M Tebbs, Christopher R Bilder

{"title":"A mixed-effects Bayesian regression model for multivariate group testing data.","authors":"Christopher S McMahan, Chase N Joyner, Joshua M Tebbs, Christopher R Bilder","doi":"10.1093/biomtc/ujaf028","DOIUrl":"10.1093/biomtc/ujaf028","url":null,"abstract":"Laboratories use group (pooled) testing with multiplex assays to reduce the time and cost associated with screening large populations for infectious diseases. Multiplex assays test for multiple diseases simultaneously, and combining their use with group testing can lead to highly efficient screening protocols. However, these benefits come at the expense of a more complex data structure which can hinder surveillance efforts. To overcome this challenge, we develop a general Bayesian framework to estimate a mixed multivariate probit model with data arising from any group testing protocol that uses multiplex assays. In the formulation of this model, we account for the correlation between true disease statuses and heterogeneity across population subgroups, and we provide for automated variable selection through the adoption of spike and slab priors. To perform model fitting, we develop an attractive posterior sampling algorithm which is straightforward to implement. We illustrate our methodology through numerical studies and analyze chlamydia and gonorrhea group testing data collected by the State Hygienic Laboratory at the University of Iowa.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673245","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