Biometrics最新文献_第2页

Adjusting for incomplete baseline covariates in randomized controlled trials: a cross-world imputation framework. 调整随机对照试验中不完整的基线协变量：跨世界估算框架。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae094

Yilin Song, James P Hughes, Ting Ye

{"title":"Adjusting for incomplete baseline covariates in randomized controlled trials: a cross-world imputation framework.","authors":"Yilin Song, James P Hughes, Ting Ye","doi":"10.1093/biomtc/ujae094","DOIUrl":"https://doi.org/10.1093/biomtc/ujae094","url":null,"abstract":"In randomized controlled trials, adjusting for baseline covariates is commonly used to improve the precision of treatment effect estimation. However, covariates often have missing values. Recently, Zhao and Ding studied two simple strategies, the single imputation method and missingness-indicator method (MIM), to handle missing covariates and showed that both methods can provide an efficiency gain compared to not adjusting for covariates. To better understand and compare these two strategies, we propose and investigate a novel theoretical imputation framework termed cross-world imputation (CWI). This framework includes both single imputation and MIM as special cases, facilitating the comparison of their efficiency. Through the lens of CWI, we show that MIM implicitly searches for the optimal CWI values and thus achieves optimal efficiency. We also derive conditions under which the single imputation method, by searching for the optimal single imputation values, can achieve the same efficiency as the MIM. We illustrate our findings through simulation studies and a real data analysis based on the Childhood Adenotonsillectomy Trial. We conclude by discussing the practical implications of our findings.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11398886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142280079","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

Hypothesis tests in ordinal predictive models with optimal accuracy. 具有最佳准确性的序数预测模型中的假设检验。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae079

Yuyang Liu, Shan Luo, Jialiang Li

{"title":"Hypothesis tests in ordinal predictive models with optimal accuracy.","authors":"Yuyang Liu, Shan Luo, Jialiang Li","doi":"10.1093/biomtc/ujae079","DOIUrl":"https://doi.org/10.1093/biomtc/ujae079","url":null,"abstract":"In real-world applications involving multi-class ordinal discrimination, a common approach is to aggregate multiple predictive variables into a linear combination, aiming to develop a classifier with high prediction accuracy. Assessment of such multi-class classifiers often utilizes the hypervolume under ROC manifolds (HUM). When dealing with a substantial pool of potential predictors and achieving optimal HUM, it becomes imperative to conduct appropriate statistical inference. However, prevalent methodologies in existing literature are computationally expensive. We propose to use the jackknife empirical likelihood method to address this issue. The Wilks' theorem under moderate conditions is established and the power analysis under the Pitman alternative is provided. We also introduce a novel network-based rapid computation algorithm specifically designed for computing a general multi-sample $U$-statistic in our test procedure. To compare our approach against existing approaches, we conduct extensive simulations. Results demonstrate the superior performance of our method in terms of test size, power, and implementation time. Furthermore, we apply our method to analyze a real medical dataset and obtain some new findings.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142016282","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

High-dimensional multivariate analysis of variance via geometric median and bootstrapping. 通过几何中值和引导进行高维多变量方差分析。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae088

Guanghui Cheng, Ruitao Lin, Liuhua Peng

{"title":"High-dimensional multivariate analysis of variance via geometric median and bootstrapping.","authors":"Guanghui Cheng, Ruitao Lin, Liuhua Peng","doi":"10.1093/biomtc/ujae088","DOIUrl":"10.1093/biomtc/ujae088","url":null,"abstract":"The geometric median, which is applicable to high-dimensional data, can be viewed as a generalization of the univariate median used in 1-dimensional data. It can be used as a robust estimator for identifying the location of multi-dimensional data and has a wide range of applications in real-world scenarios. This paper explores the problem of high-dimensional multivariate analysis of variance (MANOVA) using the geometric median. A maximum-type statistic that relies on the differences between the geometric medians among various groups is introduced. The distribution of the new test statistic is derived under the null hypothesis using Gaussian approximations, and its consistency under the alternative hypothesis is established. To approximate the distribution of the new statistic in high dimensions, a wild bootstrap algorithm is proposed and theoretically justified. Through simulation studies conducted across a variety of dimensions, sample sizes, and data-generating models, we demonstrate the finite-sample performance of our geometric median-based MANOVA method. Additionally, we implement the proposed approach to analyze a breast cancer gene expression dataset.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11381952/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142153109","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 Bayesian nonparametric approach for causal mediation with a post-treatment confounder. 贝叶斯非参数方法，用于处理后混杂因素的因果中介。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae099

Woojung Bae, Michael J Daniels, Michael G Perri

{"title":"A Bayesian nonparametric approach for causal mediation with a post-treatment confounder.","authors":"Woojung Bae, Michael J Daniels, Michael G Perri","doi":"10.1093/biomtc/ujae099","DOIUrl":"https://doi.org/10.1093/biomtc/ujae099","url":null,"abstract":"We propose a new Bayesian nonparametric method for estimating the causal effects of mediation in the presence of a post-treatment confounder. The methodology is motivated by the Rural Lifestyle Intervention Treatment Effectiveness Trial (Rural LITE) for which there is interest in estimating causal mediation effects but is complicated by the presence of a post-treatment confounder. We specify an enriched Dirichlet process mixture (EDPM) to model the joint distribution of the observed data (outcome, mediator, post-treatment confounder, treatment, and baseline confounders). For identifiability, we use the extended version of the standard sequential ignorability (SI) as introduced in Hong et al. along with a Gaussian copula model assumption. The observed data model and causal identification assumptions enable us to estimate and identify the causal effects of mediation, that is, the natural direct effects (NDE) and natural indirect effects (NIE). Our method enables easy computation of NIE and NDE for a subset of confounding variables and addresses missing data through data augmentation under the assumption of ignorable missingness. We conduct simulation studies to assess the performance of our proposed method. Furthermore, we apply this approach to evaluate the causal mediation effect in the Rural LITE trial, finding that there was not strong evidence for the potential mediator.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142280078","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

Semi-parametric benchmark dose analysis with monotone additive models. 使用单调相加模型进行半参数基准剂量分析

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae098

Alex Stringer, Tugba Akkaya Hocagil, Richard J Cook, Louise M Ryan, Sandra W Jacobson, Joseph L Jacobson

{"title":"Semi-parametric benchmark dose analysis with monotone additive models.","authors":"Alex Stringer, Tugba Akkaya Hocagil, Richard J Cook, Louise M Ryan, Sandra W Jacobson, Joseph L Jacobson","doi":"10.1093/biomtc/ujae098","DOIUrl":"https://doi.org/10.1093/biomtc/ujae098","url":null,"abstract":"Benchmark dose analysis aims to estimate the level of exposure to a toxin associated with a clinically significant adverse outcome and quantifies uncertainty using the lower limit of a confidence interval for this level. We develop a novel framework for benchmark dose analysis based on monotone additive dose-response models. We first introduce a flexible approach for fitting monotone additive models via penalized B-splines and Laplace-approximate marginal likelihood. A reflective Newton method is then developed that employs de Boor's algorithm for computing splines and their derivatives for efficient estimation of the benchmark dose. Finally, we develop a novel approach for calculating benchmark dose lower limits based on an approximate pivot for the nonlinear equation solved by the estimated benchmark dose. The favorable properties of this approach compared to the Delta method and a parameteric bootstrap are discussed. We apply the new methods to make inferences about the level of prenatal alcohol exposure associated with clinically significant cognitive defects in children using data from six NIH-funded longitudinal cohort studies. Software to reproduce the results in this paper is available online and makes use of the novel semibmd R package, which implements the methods in this paper.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11403299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142280083","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

Leveraging independence in high-dimensional mixed linear regression. 利用高维混合线性回归中的独立性

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae103

Ning Wang, Kai Deng, Qing Mai, Xin Zhang

引用次数: 0

Heterogeneous latent transfer learning in Gaussian graphical models. 高斯图形模型中的异质潜移默化学习。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae096

Qiong Wu, Chi Wang, Yong Chen

{"title":"Heterogeneous latent transfer learning in Gaussian graphical models.","authors":"Qiong Wu, Chi Wang, Yong Chen","doi":"10.1093/biomtc/ujae096","DOIUrl":"https://doi.org/10.1093/biomtc/ujae096","url":null,"abstract":"Gaussian graphical models (GGMs) are useful for understanding the complex relationships between biological entities. Transfer learning can improve the estimation of GGMs in a target dataset by incorporating relevant information from related source studies. However, biomedical research often involves intrinsic and latent heterogeneity within a study, such as heterogeneous subpopulations. This heterogeneity can make it difficult to identify informative source studies or lead to negative transfer if the source study is improperly used. To address this challenge, we developed a heterogeneous latent transfer learning (Latent-TL) approach that accounts for both within-sample and between-sample heterogeneity. The idea behind this approach is to \"learn from the alike\" by leveraging the similarities between source and target GGMs within each subpopulation. The Latent-TL algorithm simultaneously identifies common subpopulation structures among samples and facilitates the learning of target GGMs using source samples from the same subpopulation. Through extensive simulations and real data application, we have shown that the proposed method outperforms single-site learning and standard transfer learning that ignores the latent structures. We have also demonstrated the applicability of the proposed algorithm in characterizing gene co-expression networks in breast cancer patients, where the inferred genetic networks identified many biologically meaningful gene-gene interactions.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142280081","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

Nonparametric receiver operating characteristic curve analysis with an imperfect gold standard. 使用不完善的金标准进行非参数接收器工作特征曲线分析。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae063

Jiarui Sun, Chao Tang, Wuxiang Xie, Xiao-Hua Zhou

{"title":"Nonparametric receiver operating characteristic curve analysis with an imperfect gold standard.","authors":"Jiarui Sun, Chao Tang, Wuxiang Xie, Xiao-Hua Zhou","doi":"10.1093/biomtc/ujae063","DOIUrl":"https://doi.org/10.1093/biomtc/ujae063","url":null,"abstract":"This article addresses the challenge of estimating receiver operating characteristic (ROC) curves and the areas under these curves (AUC) in the context of an imperfect gold standard, a common issue in diagnostic accuracy studies. We delve into the nonparametric identification and estimation of ROC curves and AUCs when the reference standard for disease status is prone to error. Our approach hinges on the known or estimable accuracy of this imperfect reference standard and the conditional independent assumption, under which we demonstrate the identifiability of ROC curves and propose a nonparametric estimation method. In cases where the accuracy of the imperfect reference standard remains unknown, we establish that while ROC curves are unidentifiable, the sign of the difference between two AUCs is identifiable. This insight leads us to develop a hypothesis-testing method for assessing the relative superiority of AUCs. Compared to the existing methods, the proposed methods are nonparametric so that they do not rely on the parametric model assumptions. In addition, they are applicable to both the ROC/AUC analysis of continuous biomarkers and the AUC analysis of ordinal biomarkers. Our theoretical results and simulation studies validate the proposed methods, which we further illustrate through application in two real-world diagnostic studies.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141589542","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 inference for multivariate probit model with latent envelope. 具有潜在包络的多元概率模型的贝叶斯推断。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae059

Kwangmin Lee, Yeonhee Park

{"title":"Bayesian inference for multivariate probit model with latent envelope.","authors":"Kwangmin Lee, Yeonhee Park","doi":"10.1093/biomtc/ujae059","DOIUrl":"https://doi.org/10.1093/biomtc/ujae059","url":null,"abstract":"The response envelope model proposed by Cook et al. (2010) is an efficient method to estimate the regression coefficient under the context of the multivariate linear regression model. It improves estimation efficiency by identifying material and immaterial parts of responses and removing the immaterial variation. The response envelope model has been investigated only for continuous response variables. In this paper, we propose the multivariate probit model with latent envelope, in short, the probit envelope model, as a response envelope model for multivariate binary response variables. The probit envelope model takes into account relations between Gaussian latent variables of the multivariate probit model by using the idea of the response envelope model. We address the identifiability of the probit envelope model by employing the essential identifiability concept and suggest a Bayesian method for the parameter estimation. We illustrate the probit envelope model via simulation studies and real-data analysis. The simulation studies show that the probit envelope model has the potential to gain efficiency in estimation compared to the multivariate probit model. The real data analysis shows that the probit envelope model is useful for multi-label classification.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141475824","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

Nonparametric worst-case bounds for publication bias on the summary receiver operating characteristic curve. 非参数最坏情况下接收者操作特征曲线汇总的发表偏倚界限。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae080

Yi Zhou, Ao Huang, Satoshi Hattori

{"title":"Nonparametric worst-case bounds for publication bias on the summary receiver operating characteristic curve.","authors":"Yi Zhou, Ao Huang, Satoshi Hattori","doi":"10.1093/biomtc/ujae080","DOIUrl":"10.1093/biomtc/ujae080","url":null,"abstract":"The summary receiver operating characteristic (SROC) curve has been recommended as one important meta-analytical summary to represent the accuracy of a diagnostic test in the presence of heterogeneous cutoff values. However, selective publication of diagnostic studies for meta-analysis can induce publication bias (PB) on the estimate of the SROC curve. Several sensitivity analysis methods have been developed to quantify PB on the SROC curve, and all these methods utilize parametric selection functions to model the selective publication mechanism. The main contribution of this article is to propose a new sensitivity analysis approach that derives the worst-case bounds for the SROC curve by adopting nonparametric selection functions under minimal assumptions. The estimation procedures of the worst-case bounds use the Monte Carlo method to approximate the bias on the SROC curves along with the corresponding area under the curves, and then the maximum and minimum values of PB under a range of marginal selection probabilities are optimized by nonlinear programming. We apply the proposed method to real-world meta-analyses to show that the worst-case bounds of the SROC curves can provide useful insights for discussing the robustness of meta-analytical findings on diagnostic test accuracy.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142118917","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