Biometrics最新文献_第7页

Discussion on "LEAP: the latent exchangeability prior for borrowing information from historical data" by Ethan M. Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, H. Amy Xia, and Joseph G. Ibrahim. 关于 Ethan M. Alt、Xiuya Chang、Xun Jiang、Qing Liu、May Mo、H. Amy Xia 和 Joseph G. Ibrahim 所著《LEAP：从历史数据中借用信息的潜在可交换性先验》的讨论。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae086

Shannon D Thomas, Alexander M Kaizer

引用次数: 0

Rejoinder to the discussion on "LEAP: the latent exchangeability prior for borrowing information from historical data". 关于 "LEAP：从历史数据中借用信息的潜在可交换性先验 "讨论的再评论。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae087

Ethan M Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, Hong Amy Xia, Joseph G Ibrahim

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":"80 3","pages":""},"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":"80 3","pages":""},"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":"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":"80 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418020/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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":"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":"80 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11413907/pdf/","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":"OA","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":"80 3","pages":""},"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

LEAP: the latent exchangeability prior for borrowing information from historical data. LEAP：从历史数据中借用信息的潜在可交换性先验。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae083

Ethan M Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, Hong Amy Xia, Joseph G Ibrahim

{"title":"LEAP: the latent exchangeability prior for borrowing information from historical data.","authors":"Ethan M Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, Hong Amy Xia, Joseph G Ibrahim","doi":"10.1093/biomtc/ujae083","DOIUrl":"https://doi.org/10.1093/biomtc/ujae083","url":null,"abstract":"It is becoming increasingly popular to elicit informative priors on the basis of historical data. Popular existing priors, including the power prior, commensurate prior, and robust meta-analytic predictive prior, provide blanket discounting. Thus, if only a subset of participants in the historical data are exchangeable with the current data, these priors may not be appropriate. In order to combat this issue, propensity score approaches have been proposed. However, these approaches are only concerned with the covariate distribution, whereas exchangeability is typically assessed with parameters pertaining to the outcome. In this paper, we introduce the latent exchangeability prior (LEAP), where observations in the historical data are classified into exchangeable and non-exchangeable groups. The LEAP discounts the historical data by identifying the most relevant subjects from the historical data. We compare our proposed approach against alternative approaches in simulations and present a case study using our proposed prior to augment a control arm in a phase 3 clinical trial in plaque psoriasis with an unbalanced randomization scheme.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142340682","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":"80 3","pages":""},"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