Biostatistics最新文献_第7页

Improved fMRI-based pain prediction using Bayesian group-wise functional registration. 使用贝叶斯分组功能配准改进了基于fMRI的疼痛预测。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad026

Guoqing Wang, Abhirup Datta, Martin A Lindquist

{"title":"Improved fMRI-based pain prediction using Bayesian group-wise functional registration.","authors":"Guoqing Wang, Abhirup Datta, Martin A Lindquist","doi":"10.1093/biostatistics/kxad026","DOIUrl":"10.1093/biostatistics/kxad026","url":null,"abstract":"In recent years, the field of neuroimaging has undergone a paradigm shift, moving away from the traditional brain mapping approach towards the development of integrated, multivariate brain models that can predict categories of mental events. However, large interindividual differences in both brain anatomy and functional localization after standard anatomical alignment remain a major limitation in performing this type of analysis, as it leads to feature misalignment across subjects in subsequent predictive models. This article addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subject's functional data to a common latent template map. Our proposed Bayesian functional group-wise registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. We achieve the probabilistic registration with inverse-consistency by utilizing the generalized Bayes framework with a loss function for the symmetric group-wise registration. It models the latent template with a Gaussian process, which helps capture spatial features in the template, producing a more precise estimation. We evaluate the method in simulation studies and apply it to data from an fMRI study of thermal pain, with the goal of using functional brain activity to predict physical pain. We find that the proposed approach allows for improved prediction of reported pain scores over conventional approaches. Received on 2 January 2017. Editorial decision on 8 June 2021.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"885-903"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41170769","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

An intersectional framework for counterfactual fairness in risk prediction. 风险预测中反事实公平性的交叉框架。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad021

Solvejg Wastvedt, Jared D Huling, Julian Wolfson

{"title":"An intersectional framework for counterfactual fairness in risk prediction.","authors":"Solvejg Wastvedt, Jared D Huling, Julian Wolfson","doi":"10.1093/biostatistics/kxad021","DOIUrl":"10.1093/biostatistics/kxad021","url":null,"abstract":"Along with the increasing availability of health data has come the rise of data-driven models to inform decision making and policy. These models have the potential to benefit both patients and health care providers but can also exacerbate health inequities. Existing \"algorithmic fairness\" methods for measuring and correcting model bias fall short of what is needed for health policy in two key ways. First, methods typically focus on a single grouping along which discrimination may occur rather than considering multiple, intersecting groups. Second, in clinical applications, risk prediction is typically used to guide treatment, creating distinct statistical issues that invalidate most existing techniques. We present novel unfairness metrics that address both challenges. We also develop a complete framework of estimation and inference tools for our metrics, including the unfairness value (\"u-value\"), used to determine the relative extremity of unfairness, and standard errors and confidence intervals employing an alternative to the standard bootstrap. We demonstrate application of our framework to a COVID-19 risk prediction model deployed in a major Midwestern health system.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"702-717"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10499741","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

DP2LM: leveraging deep learning approach for estimation and hypothesis testing on mediation effects with high-dimensional mediators and complex confounders. DP2LM：利用深度学习方法对具有高维中介因素和复杂混杂因素的中介效应进行估计和假设检验。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad037

Shuoyang Wang, Yuan Huang

{"title":"DP2LM: leveraging deep learning approach for estimation and hypothesis testing on mediation effects with high-dimensional mediators and complex confounders.","authors":"Shuoyang Wang, Yuan Huang","doi":"10.1093/biostatistics/kxad037","DOIUrl":"10.1093/biostatistics/kxad037","url":null,"abstract":"Traditional linear mediation analysis has inherent limitations when it comes to handling high-dimensional mediators. Particularly, accurately estimating and rigorously inferring mediation effects is challenging, primarily due to the intertwined nature of the mediator selection issue. Despite recent developments, the existing methods are inadequate for addressing the complex relationships introduced by confounders. To tackle these challenges, we propose a novel approach called DP2LM (Deep neural network-based Penalized Partially Linear Mediation). This approach incorporates deep neural network techniques to account for nonlinear effects in confounders and utilizes the penalized partially linear model to accommodate high dimensionality. Unlike most existing works that concentrate on mediator selection, our method prioritizes estimation and inference on mediation effects. Specifically, we develop test procedures for testing the direct and indirect mediation effects. Theoretical analysis shows that the tests maintain the Type-I error rate. In simulation studies, DP2LM demonstrates its superior performance as a modeling tool for complex data, outperforming existing approaches in a wide range of settings and providing reliable estimation and inference in scenarios involving a considerable number of mediators. Further, we apply DP2LM to investigate the mediation effect of DNA methylation on cortisol stress reactivity in individuals who experienced childhood trauma, uncovering new insights through a comprehensive analysis.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"818-832"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139708546","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

Bayesian joint models for multi-regional clinical trials. 用于多地区临床试验的贝叶斯联合模型。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad023

Nathan W Bean, Joseph G Ibrahim, Matthew A Psioda

{"title":"Bayesian joint models for multi-regional clinical trials.","authors":"Nathan W Bean, Joseph G Ibrahim, Matthew A Psioda","doi":"10.1093/biostatistics/kxad023","DOIUrl":"10.1093/biostatistics/kxad023","url":null,"abstract":"In recent years, multi-regional clinical trials (MRCTs) have increased in popularity in the pharmaceutical industry due to their ability to accelerate the global drug development process. To address potential challenges with MRCTs, the International Council for Harmonisation released the E17 guidance document which suggests the use of statistical methods that utilize information borrowing across regions if regional sample sizes are small. We develop an approach that allows for information borrowing via Bayesian model averaging in the context of a joint analysis of survival and longitudinal data from MRCTs. In this novel application of joint models to MRCTs, we use Laplace's method to integrate over subject-specific random effects and to approximate posterior distributions for region-specific treatment effects on the time-to-event outcome. Through simulation studies, we demonstrate that the joint modeling approach can result in an increased rejection rate when testing the global treatment effect compared with methods that analyze survival data alone. We then apply the proposed approach to data from a cardiovascular outcomes MRCT.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"852-866"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11247186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10152517","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

A Bayesian nonparametric approach to correct for underreporting in count data. 一种贝叶斯非参数方法，用于纠正计数数据中的漏报。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad027

Serena Arima, Silvia Polettini, Giuseppe Pasculli, Loreto Gesualdo, Francesco Pesce, Deni-Aldo Procaccini

{"title":"A Bayesian nonparametric approach to correct for underreporting in count data.","authors":"Serena Arima, Silvia Polettini, Giuseppe Pasculli, Loreto Gesualdo, Francesco Pesce, Deni-Aldo Procaccini","doi":"10.1093/biostatistics/kxad027","DOIUrl":"10.1093/biostatistics/kxad027","url":null,"abstract":"We propose a nonparametric compound Poisson model for underreported count data that introduces a latent clustering structure for the reporting probabilities. The latter are estimated with the model's parameters based on experts' opinion and exploiting a proxy for the reporting process. The proposed model is used to estimate the prevalence of chronic kidney disease in Apulia, Italy, based on a unique statistical database covering information on m = 258 municipalities obtained by integrating multisource register information. Accurate prevalence estimates are needed for monitoring, surveillance, and management purposes; yet, counts are deemed to be considerably underreported, especially in some areas of Apulia, one of the most deprived and heterogeneous regions in Italy. Our results agree with previous findings and highlight interesting geographical patterns of the disease. We compare our model to existing approaches in the literature using simulated as well as real data on early neonatal mortality risk in Brazil, described in previous research: the proposed approach proves to be accurate and particularly suitable when partial information about data quality is available.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"904-918"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41161396","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

Analyzing microbial evolution through gene and genome phylogenies. 通过基因和基因组系统发育分析微生物进化。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad025

Sarah Teichman, Michael D Lee, Amy D Willis

{"title":"Analyzing microbial evolution through gene and genome phylogenies.","authors":"Sarah Teichman, Michael D Lee, Amy D Willis","doi":"10.1093/biostatistics/kxad025","DOIUrl":"10.1093/biostatistics/kxad025","url":null,"abstract":"Microbiome scientists critically need modern tools to explore and analyze microbial evolution. Often this involves studying the evolution of microbial genomes as a whole. However, different genes in a single genome can be subject to different evolutionary pressures, which can result in distinct gene-level evolutionary histories. To address this challenge, we propose to treat estimated gene-level phylogenies as data objects, and present an interactive method for the analysis of a collection of gene phylogenies. We use a local linear approximation of phylogenetic tree space to visualize estimated gene trees as points in low-dimensional Euclidean space, and address important practical limitations of existing related approaches, allowing an intuitive visualization of complex data objects. We demonstrate the utility of our proposed approach through microbial data analyses, including by identifying outlying gene histories in strains of Prevotella, and by contrasting Streptococcus phylogenies estimated using different gene sets. Our method is available as an open-source R package, and assists with estimating, visualizing, and interacting with a collection of bacterial gene phylogenies.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"786-800"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11247178/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66784613","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

A Bayesian nonparametric approach for multiple mediators with applications in mental health studies. 应用于心理健康研究的贝叶斯非参数多重中介方法。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad038

Samrat Roy, Michael J Daniels, Jason Roy

{"title":"A Bayesian nonparametric approach for multiple mediators with applications in mental health studies.","authors":"Samrat Roy, Michael J Daniels, Jason Roy","doi":"10.1093/biostatistics/kxad038","DOIUrl":"10.1093/biostatistics/kxad038","url":null,"abstract":"Mediation analysis with contemporaneously observed multiple mediators is a significant area of causal inference. Recent approaches for multiple mediators are often based on parametric models and thus may suffer from model misspecification. Also, much of the existing literature either only allow estimation of the joint mediation effect or estimate the joint mediation effect just as the sum of individual mediator effects, ignoring the interaction among the mediators. In this article, we propose a novel Bayesian nonparametric method that overcomes the two aforementioned drawbacks. We model the joint distribution of the observed data (outcome, mediators, treatment, and confounders) flexibly using an enriched Dirichlet process mixture with three levels. We use standardization (g-computation) to compute all possible mediation effects, including pairwise and all other possible interaction among the mediators. We thoroughly explore our method via simulations and apply our method to a mental health data from Wisconsin Longitudinal Study, where we estimate how the effect of births from unintended pregnancies on later life mental depression (CES-D) among the mothers is mediated through lack of self-acceptance and autonomy, employment instability, lack of social participation, and increased family stress. Our method identified significant individual mediators, along with some significant pairwise effects.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"919-932"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11247183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139708545","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

Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms. 离散结果的高维度变量选择个性化治疗规则：降低抑郁症状的严重程度。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad022

Erica E M Moodie, Zeyu Bian, Janie Coulombe, Yi Lian, Archer Y Yang, Susan M Shortreed

引用次数: 0

An integrative latent class model of heterogeneous data modalities for diagnosing kidney obstruction. 诊断肾梗阻的异质性数据模式的综合潜在类模型。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad020

Jeong Hoon Jang, Changgee Chang, Amita K Manatunga, Andrew T Taylor, Qi Long

{"title":"An integrative latent class model of heterogeneous data modalities for diagnosing kidney obstruction.","authors":"Jeong Hoon Jang, Changgee Chang, Amita K Manatunga, Andrew T Taylor, Qi Long","doi":"10.1093/biostatistics/kxad020","DOIUrl":"10.1093/biostatistics/kxad020","url":null,"abstract":"Radionuclide imaging plays a critical role in the diagnosis and management of kidney obstruction. However, most practicing radiologists in US hospitals have insufficient time and resources to acquire training and experience needed to interpret radionuclide images, leading to increased diagnostic errors. To tackle this problem, Emory University embarked on a study that aims to develop a computer-assisted diagnostic (CAD) tool for kidney obstruction by mining and analyzing patient data comprised of renogram curves, ordinal expert ratings on the obstruction status, pharmacokinetic variables, and demographic information. The major challenges here are the heterogeneity in data modes and the lack of gold standard for determining kidney obstruction. In this article, we develop a statistically principled CAD tool based on an integrative latent class model that leverages heterogeneous data modalities available for each patient to provide accurate prediction of kidney obstruction. Our integrative model consists of three sub-models (multilevel functional latent factor regression model, probit scalar-on-function regression model, and Gaussian mixture model), each of which is tailored to the specific data mode and depends on the unknown obstruction status (latent class). An efficient MCMC algorithm is developed to train the model and predict kidney obstruction with associated uncertainty. Extensive simulations are conducted to evaluate the performance of the proposed method. An application to an Emory renal study demonstrates the usefulness of our model as a CAD tool for kidney obstruction.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"769-785"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11247177/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10252590","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

Quantification and statistical modeling of droplet-based single-nucleus RNA-sequencing data. 基于液滴的单核rna测序数据的量化和统计建模。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad010

Albert Kuo, Kasper D Hansen, Stephanie C Hicks

{"title":"Quantification and statistical modeling of droplet-based single-nucleus RNA-sequencing data.","authors":"Albert Kuo, Kasper D Hansen, Stephanie C Hicks","doi":"10.1093/biostatistics/kxad010","DOIUrl":"10.1093/biostatistics/kxad010","url":null,"abstract":"In complex tissues containing cells that are difficult to dissociate, single-nucleus RNA-sequencing (snRNA-seq) has become the preferred experimental technology over single-cell RNA-sequencing (scRNA-seq) to measure gene expression. To accurately model these data in downstream analyses, previous work has shown that droplet-based scRNA-seq data are not zero-inflated, but whether droplet-based snRNA-seq data follow the same probability distributions has not been systematically evaluated. Using pseudonegative control data from nuclei in mouse cortex sequenced with the 10x Genomics Chromium system and mouse kidney sequenced with the DropSeq system, we found that droplet-based snRNA-seq data follow a negative binomial distribution, suggesting that parametric statistical models applied to scRNA-seq are transferable to snRNA-seq. Furthermore, we found that the quantification choices in adapting quantification mapping strategies from scRNA-seq to snRNA-seq can play a significant role in downstream analyses and biological interpretation. In particular, reference transcriptomes that do not include intronic regions result in significantly smaller library sizes and incongruous cell type classifications. We also confirmed the presence of a gene length bias in snRNA-seq data, which we show is present in both exonic and intronic reads, and investigate potential causes for the bias.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"801-817"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11247185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9551865","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