Biostatistics最新文献_第2页

Mendelian randomization analysis using multiple biomarkers of an underlying common exposure. 利用潜在共同暴露的多种生物标志物进行孟德尔随机分析。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxae006

Jin Jin, Guanghao Qi, Zhi Yu, Nilanjan Chatterjee

{"title":"Mendelian randomization analysis using multiple biomarkers of an underlying common exposure.","authors":"Jin Jin, Guanghao Qi, Zhi Yu, Nilanjan Chatterjee","doi":"10.1093/biostatistics/kxae006","DOIUrl":"10.1093/biostatistics/kxae006","url":null,"abstract":"Mendelian randomization (MR) analysis is increasingly popular for testing the causal effect of exposures on disease outcomes using data from genome-wide association studies. In some settings, the underlying exposure, such as systematic inflammation, may not be directly observable, but measurements can be available on multiple biomarkers or other types of traits that are co-regulated by the exposure. We propose a method for MR analysis on latent exposures (MRLE), which tests the significance for, and the direction of, the effect of a latent exposure by leveraging information from multiple related traits. The method is developed by constructing a set of estimating functions based on the second-order moments of GWAS summary association statistics for the observable traits, under a structural equation model where genetic variants are assumed to have indirect effects through the latent exposure and potentially direct effects on the traits. Simulation studies show that MRLE has well-controlled type I error rates and enhanced power compared to single-trait MR tests under various types of pleiotropy. Applications of MRLE using genetic association statistics across five inflammatory biomarkers (CRP, IL-6, IL-8, TNF-α, and MCP-1) provide evidence for potential causal effects of inflammation on increasing the risk of coronary artery disease, colorectal cancer, and rheumatoid arthritis, while standard MR analysis for individual biomarkers fails to detect consistent evidence for such effects.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140066298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Bayesian approach for investigating the pharmacogenetics of combination antiretroviral therapy in people with HIV. 研究艾滋病病毒感染者抗逆转录病毒联合疗法药物遗传学的贝叶斯方法。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxae001

Wei Jin, Yang Ni, Amanda B Spence, Leah H Rubin, Yanxun Xu

{"title":"A Bayesian approach for investigating the pharmacogenetics of combination antiretroviral therapy in people with HIV.","authors":"Wei Jin, Yang Ni, Amanda B Spence, Leah H Rubin, Yanxun Xu","doi":"10.1093/biostatistics/kxae001","DOIUrl":"10.1093/biostatistics/kxae001","url":null,"abstract":"Combination antiretroviral therapy (ART) with at least three different drugs has become the standard of care for people with HIV (PWH) due to its exceptional effectiveness in viral suppression. However, many ART drugs have been reported to associate with neuropsychiatric adverse effects including depression, especially when certain genetic polymorphisms exist. Pharmacogenetics is an important consideration for administering combination ART as it may influence drug efficacy and increase risk for neuropsychiatric conditions. Large-scale longitudinal HIV databases provide researchers opportunities to investigate the pharmacogenetics of combination ART in a data-driven manner. However, with more than 30 FDA-approved ART drugs, the interplay between the large number of possible ART drug combinations and genetic polymorphisms imposes statistical modeling challenges. We develop a Bayesian approach to examine the longitudinal effects of combination ART and their interactions with genetic polymorphisms on depressive symptoms in PWH. The proposed method utilizes a Gaussian process with a composite kernel function to capture the longitudinal combination ART effects by directly incorporating individuals' treatment histories, and a Bayesian classification and regression tree to account for individual heterogeneity. Through both simulation studies and an application to a dataset from the Women's Interagency HIV Study, we demonstrate the clinical utility of the proposed approach in investigating the pharmacogenetics of combination ART and assisting physicians to make effective individualized treatment decisions that can improve health outcomes for PWH.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139747854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy. 树状信息贝叶斯多源领域适应：利用口头尸检进行跨人群死因概率分配。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxae005

Zhenke Wu, Zehang R Li, Irena Chen, Mengbing Li

{"title":"Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy.","authors":"Zhenke Wu, Zehang R Li, Irena Chen, Mengbing Li","doi":"10.1093/biostatistics/kxae005","DOIUrl":"10.1093/biostatistics/kxae005","url":null,"abstract":"Determining causes of deaths (CODs) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of interviewing relatives of a deceased person about symptoms of the deceased in the period leading to the death, often resulting in multivariate binary responses. While statistical methods have been devised for estimating the cause-specific mortality fractions (CSMFs) for a study population, continued expansion of VA to new populations (or \"domains\") necessitates approaches that recognize between-domain differences while capitalizing on potential similarities. In this article, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a prespecified rooted weighted tree. Given a cause, we use latent class models to characterize the conditional distributions of the responses that may vary by domain. We specify a logistic stick-breaking Gaussian diffusion process prior along the tree for class mixing weights with node-specific spike-and-slab priors to pool information between the domains in a data-driven way. The posterior inference is conducted via a scalable variational Bayes algorithm. Simulation studies show that the domain adaptation enabled by the proposed method improves CSMF estimation and individual COD assignment. We also illustrate and evaluate the method using a validation dataset. The article concludes with a discussion of limitations and future directions.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139944717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Fast matrix completion in epigenetic methylation studies with informative covariates. 在带有信息协变量的表观遗传甲基化研究中快速完成矩阵。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxae016

Mélina Ribaud, Aurélie Labbe, Khaled Fouda, Karim Oualkacha

{"title":"Fast matrix completion in epigenetic methylation studies with informative covariates.","authors":"Mélina Ribaud, Aurélie Labbe, Khaled Fouda, Karim Oualkacha","doi":"10.1093/biostatistics/kxae016","DOIUrl":"10.1093/biostatistics/kxae016","url":null,"abstract":"DNA methylation is an important epigenetic mark that modulates gene expression through the inhibition of transcriptional proteins binding to DNA. As in many other omics experiments, the issue of missing values is an important one, and appropriate imputation techniques are important in avoiding an unnecessary sample size reduction as well as to optimally leverage the information collected. We consider the case where relatively few samples are processed via an expensive high-density whole genome bisulfite sequencing (WGBS) strategy and a larger number of samples is processed using more affordable low-density, array-based technologies. In such cases, one can impute the low-coverage (array-based) methylation data using the high-density information provided by the WGBS samples. In this paper, we propose an efficient Linear Model of Coregionalisation with informative Covariates (LMCC) to predict missing values based on observed values and covariates. Our model assumes that at each site, the methylation vector of all samples is linked to the set of fixed factors (covariates) and a set of latent factors. Furthermore, we exploit the functional nature of the data and the spatial correlation across sites by assuming some Gaussian processes on the fixed and latent coefficient vectors, respectively. Our simulations show that the use of covariates can significantly improve the accuracy of imputed values, especially in cases where missing data contain some relevant information about the explanatory variable. We also showed that our proposed model is particularly efficient when the number of columns is much greater than the number of rows-which is usually the case in methylation data analysis. Finally, we apply and compare our proposed method with alternative approaches on two real methylation datasets, showing how covariates such as cell type, tissue type or age can enhance the accuracy of imputed values.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471954/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141293984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Neuroimaging meta regression for coordinate based meta analysis data with a spatial model. 利用空间模型对基于坐标的元分析数据进行神经成像元回归。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxae024

Yifan Yu, Rosario Pintos Lobo, Michael Cody Riedel, Katherine Bottenhorn, Angela R Laird, Thomas E Nichols

{"title":"Neuroimaging meta regression for coordinate based meta analysis data with a spatial model.","authors":"Yifan Yu, Rosario Pintos Lobo, Michael Cody Riedel, Katherine Bottenhorn, Angela R Laird, Thomas E Nichols","doi":"10.1093/biostatistics/kxae024","DOIUrl":"10.1093/biostatistics/kxae024","url":null,"abstract":"Coordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In this article, we propose a generative coordinate-based meta-regression (CBMR) framework to approximate a smooth activation intensity function and investigate the effect of study-level covariates (e.g. year of publication, sample size). We employ a spline parameterization to model the spatial structure of brain activation and consider four stochastic models for modeling the random variation in foci. To examine the validity of CBMR, we estimate brain activation on 20 meta-analytic datasets, conduct spatial homogeneity tests at the voxel level, and compare the results to those generated by existing kernel-based and model-based approaches. Keywords: generalized linear models; meta-analysis; spatial statistics; statistical modeling.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471956/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141604512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 models augmented by hierarchical data: an application of estimating HIV epidemics at sub-national level. 分层数据增强的动态模型：估算国家以下一级艾滋病毒流行情况的应用。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxae003

Bao Le, Xiaoyue Niu, Tim Brown, Jeffrey W Imai-Eaton

{"title":"Dynamic models augmented by hierarchical data: an application of estimating HIV epidemics at sub-national level.","authors":"Bao Le, Xiaoyue Niu, Tim Brown, Jeffrey W Imai-Eaton","doi":"10.1093/biostatistics/kxae003","DOIUrl":"10.1093/biostatistics/kxae003","url":null,"abstract":"Dynamic models have been successfully used in producing estimates of HIV epidemics at the national level due to their epidemiological nature and their ability to estimate prevalence, incidence, and mortality rates simultaneously. Recently, HIV interventions and policies have required more information at sub-national levels to support local planning, decision-making and resource allocation. Unfortunately, many areas lack sufficient data for deriving stable and reliable results, and this is a critical technical barrier to more stratified estimates. One solution is to borrow information from other areas within the same country. However, directly assuming hierarchical structures within the HIV dynamic models is complicated and computationally time-consuming. In this article, we propose a simple and innovative way to incorporate hierarchical information into the dynamical systems by using auxiliary data. The proposed method efficiently uses information from multiple areas within each country without increasing the computational burden. As a result, the new model improves predictive ability and uncertainty assessment.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139998375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Bayesian mixed model inference for genetic association under related samples with brain network phenotype. 贝叶斯混合模型推断脑网络表型相关样本下的遗传关联。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxae008

Xinyuan Tian, Yiting Wang, Selena Wang, Yi Zhao, Yize Zhao

{"title":"Bayesian mixed model inference for genetic association under related samples with brain network phenotype.","authors":"Xinyuan Tian, Yiting Wang, Selena Wang, Yi Zhao, Yize Zhao","doi":"10.1093/biostatistics/kxae008","DOIUrl":"10.1093/biostatistics/kxae008","url":null,"abstract":"Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140144658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 predictors of resilience to stressors in single-arm studies of pre-post change. 在前后变化的单臂研究中确定对压力的恢复能力的预测因素。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxad018

Ravi Varadhan, Jiafeng Zhu, Karen Bandeen-Roche

{"title":"Identifying predictors of resilience to stressors in single-arm studies of pre-post change.","authors":"Ravi Varadhan, Jiafeng Zhu, Karen Bandeen-Roche","doi":"10.1093/biostatistics/kxad018","DOIUrl":"10.1093/biostatistics/kxad018","url":null,"abstract":"Many older adults experience a major stressor at some point in their lives. The ability to recover well after a major stressor is known as resilience. An important goal of geriatric research is to identify factors that influence resilience to stressors. Studies of resilience in older adults are typically conducted with a single-arm where everyone experiences the stressor. The simplistic approach of regressing change versus baseline yields biased estimates due to mathematical coupling and regression to the mean (RTM). We develop a method to correct the bias. We extend the method to include covariates. Our approach considers a counterfactual control group and involves sensitivity analyses to evaluate different settings of control group parameters. Only minimal distributional assumptions are required. Simulation studies demonstrate the validity of the method. We illustrate the method using a large, registry of older adults (N =7239) who underwent total knee replacement (TKR). We demonstrate how external data can be utilized to constrain the sensitivity analysis. Naive analyses implicated several treatment effect modifiers including baseline function, age, body-mass index (BMI), gender, number of comorbidities, income, and race. Corrected analysis revealed that baseline (pre-stressor) function was not strongly linked to recovery after TKR and among the covariates, only age and number of comorbidities were consistently and negatively associated with post-stressor recovery in all functional domains. Correction of mathematical coupling and RTM is necessary for drawing valid inferences regarding the effect of covariates and baseline status on pre-post change. Our method provides a simple estimator to this end.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10297247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Similarity-based multimodal regression. 基于相似性的多模态回归。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxad033

Andrew A Chen, Sarah M Weinstein, Azeez Adebimpe, Ruben C Gur, Raquel E Gur, Kathleen R Merikangas, Theodore D Satterthwaite, Russell T Shinohara, Haochang Shou

{"title":"Similarity-based multimodal regression.","authors":"Andrew A Chen, Sarah M Weinstein, Azeez Adebimpe, Ruben C Gur, Raquel E Gur, Kathleen R Merikangas, Theodore D Satterthwaite, Russell T Shinohara, Haochang Shou","doi":"10.1093/biostatistics/kxad033","DOIUrl":"10.1093/biostatistics/kxad033","url":null,"abstract":"To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain comparable features for a combined analysis. Multimodal data fusion enables certain analyses on matrix-valued and vector-valued data, but it generally cannot integrate modalities of different dimensions and data structures. For a single data modality, multivariate distance matrix regression provides a distance-based framework for regression accommodating a wide range of data types. However, no distance-based method exists to handle multiple complementary types of data. We propose a novel distance-based regression model, which we refer to as Similarity-based Multimodal Regression (SiMMR), that enables simultaneous regression of multiple modalities through their distance profiles. We demonstrate through simulation, imaging studies, and longitudinal mobile health analyses that our proposed method can detect associations between clinical variables and multimodal data of differing properties and dimensionalities, even with modest sample sizes. We perform experiments to evaluate several different test statistics and provide recommendations for applying our method across a broad range of scenarios.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138500309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Evaluating dynamic and predictive discrimination for recurrent event models: use of a time-dependent C-index. 评估动态和预测判别的反复事件模型:使用时间相关的c指数。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxad031

Jian Wang, Xinyang Jiang, Jing Ning

{"title":"Evaluating dynamic and predictive discrimination for recurrent event models: use of a time-dependent C-index.","authors":"Jian Wang, Xinyang Jiang, Jing Ning","doi":"10.1093/biostatistics/kxad031","DOIUrl":"10.1093/biostatistics/kxad031","url":null,"abstract":"Interest in analyzing recurrent event data has increased over the past few decades. One essential aspect of a risk prediction model for recurrent event data is to accurately distinguish individuals with different risks of developing a recurrent event. Although the concordance index (C-index) effectively evaluates the overall discriminative ability of a regression model for recurrent event data, a local measure is also desirable to capture dynamic performance of the regression model over time. Therefore, in this study, we propose a time-dependent C-index measure for inferring the model's discriminative ability locally. We formulated the C-index as a function of time using a flexible parametric model and constructed a concordance-based likelihood for estimation and inference. We adapted a perturbation-resampling procedure for variance estimation. Extensive simulations were conducted to investigate the proposed time-dependent C-index's finite-sample performance and estimation procedure. We applied the time-dependent C-index to three regression models of a study of re-hospitalization in patients with colorectal cancer to evaluate the models' discriminative capability.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471962/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89720651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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