Biometrics最新文献_第3页

A unified combination framework for dependent tests with applications to microbiome association studies.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf001

Xiufan Yu, Linjun Zhang, Arun Srinivasan, Min-Ge Xie, Lingzhou Xue

{"title":"A unified combination framework for dependent tests with applications to microbiome association studies.","authors":"Xiufan Yu, Linjun Zhang, Arun Srinivasan, Min-Ge Xie, Lingzhou Xue","doi":"10.1093/biomtc/ujaf001","DOIUrl":"10.1093/biomtc/ujaf001","url":null,"abstract":"We introduce a novel meta-analysis framework to combine dependent tests under a general setting, and utilize it to synthesize various microbiome association tests that are calculated from the same dataset. Our development builds upon the classical meta-analysis methods of aggregating P-values and also a more recent general method of combining confidence distributions, but makes generalizations to handle dependent tests. The proposed framework ensures rigorous statistical guarantees, and we provide a comprehensive study and compare it with various existing dependent combination methods. Notably, we demonstrate that the widely used Cauchy combination method for dependent tests, referred to as the vanilla Cauchy combination in this article, can be viewed as a special case within our framework. Moreover, the proposed framework provides a way to address the problem when the distributional assumptions underlying the vanilla Cauchy combination are violated. Our numerical results demonstrate that ignoring the dependence among the to-be-combined components may lead to a severe size distortion phenomenon. Compared to the existing P-value combination methods, including the vanilla Cauchy combination method and other methods, the proposed combination framework is flexible and can be adapted to handle the dependence accurately and utilizes the information efficiently to construct tests with accurate size and enhanced power. The development is applied to the microbiome association studies, where we aggregate information from multiple existing tests using the same dataset. The combined tests harness the strengths of each individual test across a wide range of alternative spaces, enabling more efficient and meaningful discoveries of vital microbiome associations.","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/PMC11783248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143063363","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

Combining experimental and observational data through a power likelihood.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf008

Xi Lin, Jens Magelund Tarp, Robin J Evans

{"title":"Combining experimental and observational data through a power likelihood.","authors":"Xi Lin, Jens Magelund Tarp, Robin J Evans","doi":"10.1093/biomtc/ujaf008","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf008","url":null,"abstract":"Randomized controlled trials are the gold standard for causal inference and play a pivotal role in modern evidence-based medicine. However, the sample sizes they use are often too limited to provide adequate power for drawing causal conclusions. In contrast, observational data are becoming increasingly accessible in large volumes but can be subject to bias as a result of hidden confounding. Given these complementary features, we propose a power likelihood approach to augmenting randomized controlled trials with observational data to improve the efficiency of treatment effect estimation. We provide a data-adaptive procedure for maximizing the expected log predictive density (ELPD) to select the learning rate that best regulates the information from the observational data. We validate our method through a simulation study that shows increased power while maintaining an approximate nominal coverage rate. Finally, we apply our method in a real-world data fusion study augmenting the PIONEER 6 clinical trial with a US health claims dataset, demonstrating the effectiveness of our method and providing detailed guidance on how to address practical considerations in its application.","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":"143432300","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 regularized Bayesian Dirichlet-multinomial regression model for integrating single-cell-level omics and patient-level clinical study data.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf005

Yanghong Guo, Lei Yu, Lei Guo, Lin Xu, Qiwei Li

{"title":"A regularized Bayesian Dirichlet-multinomial regression model for integrating single-cell-level omics and patient-level clinical study data.","authors":"Yanghong Guo, Lei Yu, Lei Guo, Lin Xu, Qiwei Li","doi":"10.1093/biomtc/ujaf005","DOIUrl":"10.1093/biomtc/ujaf005","url":null,"abstract":"The abundance of various cell types can vary significantly among patients with varying phenotypes and even those with the same phenotype. Recent scientific advancements provide mounting evidence that other clinical variables, such as age, gender, and lifestyle habits, can also influence the abundance of certain cell types. However, current methods for integrating single-cell-level omics data with clinical variables are inadequate. In this study, we propose a regularized Bayesian Dirichlet-multinomial regression framework to investigate the relationship between single-cell RNA sequencing data and patient-level clinical data. Additionally, the model employs a novel hierarchical tree structure to identify such relationships at different cell-type levels. Our model successfully uncovers significant associations between specific cell types and clinical variables across three distinct diseases: pulmonary fibrosis, COVID-19, and non-small cell lung cancer. This integrative analysis provides biological insights and could potentially inform clinical interventions for various diseases.","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/PMC11783250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143063282","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

Individualized multi-treatment response curves estimation using RBF-net with shared neurons.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf019

Peter Chang, Arkaprava Roy

引用次数: 0

Multiply robust difference-in-differences estimation of causal effect curves for continuous exposures.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf015

Gary Hettinger, Youjin Lee, Nandita Mitra

{"title":"Multiply robust difference-in-differences estimation of causal effect curves for continuous exposures.","authors":"Gary Hettinger, Youjin Lee, Nandita Mitra","doi":"10.1093/biomtc/ujaf015","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf015","url":null,"abstract":"Researchers commonly use difference-in-differences (DiD) designs to evaluate public policy interventions. While methods exist for estimating effects in the context of binary interventions, policies often result in varied exposures across regions implementing the policy. Yet, existing approaches for incorporating continuous exposures face substantial limitations in addressing confounding variables associated with intervention status, exposure levels, and outcome trends. These limitations significantly constrain policymakers' ability to fully comprehend policy impacts and design future interventions. In this work, we propose new estimators for causal effect curves within the DiD framework, accounting for multiple sources of confounding. Our approach accommodates misspecification of a subset of intervention, exposure, and outcome models while avoiding any parametric assumptions on the effect curve. We present the statistical properties of the proposed methods and illustrate their application through simulations and a study investigating the heterogeneous effects of a nutritional excise tax under different levels of accessibility to cross-border shopping.","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":"143482119","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 dyadic models for spatio-temporal data. 时空数据的复合二元模型。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae107

Michael R Schwob, Mevin B Hooten, Vagheesh Narasimhan

引用次数: 0

Group sequential testing of a treatment effect using a surrogate marker. 使用替代标记对治疗效果进行分组序列测试。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae108

Layla Parast, Jay Bartroff

{"title":"Group sequential testing of a treatment effect using a surrogate marker.","authors":"Layla Parast, Jay Bartroff","doi":"10.1093/biomtc/ujae108","DOIUrl":"10.1093/biomtc/ujae108","url":null,"abstract":"The identification of surrogate markers is motivated by their potential to make decisions sooner about a treatment effect. However, few methods have been developed to actually use a surrogate marker to test for a treatment effect in a future study. Most existing methods consider combining surrogate marker and primary outcome information to test for a treatment effect, rely on fully parametric methods where strict parametric assumptions are made about the relationship between the surrogate and the outcome, and/or assume the surrogate marker is measured at only a single time point. Recent work has proposed a nonparametric test for a treatment effect using only surrogate marker information measured at a single time point by borrowing information learned from a prior study where both the surrogate and primary outcome were measured. In this paper, we utilize this nonparametric test and propose group sequential procedures that allow for early stopping of treatment effect testing in a setting where the surrogate marker is measured repeatedly over time. We derive the properties of the correlated surrogate-based nonparametric test statistics at multiple time points and compute stopping boundaries that allow for early stopping for a significant treatment effect, or for futility. We examine the performance of our proposed test using a simulation study and illustrate the method using data from two distinct AIDS clinical trials.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11459368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142387635","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

How to achieve model-robust inference in stepped wedge trials with model-based methods? 如何利用基于模型的方法在阶梯楔形试验中实现模型可靠的推断？

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae123

Bingkai Wang, Xueqi Wang, Fan Li

{"title":"How to achieve model-robust inference in stepped wedge trials with model-based methods?","authors":"Bingkai Wang, Xueqi Wang, Fan Li","doi":"10.1093/biomtc/ujae123","DOIUrl":"10.1093/biomtc/ujae123","url":null,"abstract":"A stepped wedge design is an unidirectional crossover design where clusters are randomized to distinct treatment sequences. While model-based analysis of stepped wedge designs is a standard practice to evaluate treatment effects accounting for clustering and adjusting for covariates, their properties under misspecification have not been systematically explored. In this article, we focus on model-based methods, including linear mixed models and generalized estimating equations with an independence, simple exchangeable, or nested exchangeable working correlation structure. We study when a potentially misspecified working model can offer consistent estimation of the marginal treatment effect estimands, which are defined nonparametrically with potential outcomes and may be functions of calendar time and/or exposure time. We prove a central result that consistency for nonparametric estimands usually requires a correctly specified treatment effect structure, but generally not the remaining aspects of the working model (functional form of covariates, random effects, and error distribution), and valid inference is obtained via the sandwich variance estimator. Furthermore, an additional g-computation step is required to achieve model-robust inference under non-identity link functions or for ratio estimands. The theoretical results are illustrated via several simulation experiments and re-analysis of a completed stepped wedge cluster randomized trial.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11536888/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142581068","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

Heterogeneity-aware integrative regression for ancestry-specific association studies. 用于祖先特异性关联研究的异质性感知整合回归。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae109

Aaron J Molstad, Yanwei Cai, Alexander P Reiner, Charles Kooperberg, Wei Sun, Li Hsu

{"title":"Heterogeneity-aware integrative regression for ancestry-specific association studies.","authors":"Aaron J Molstad, Yanwei Cai, Alexander P Reiner, Charles Kooperberg, Wei Sun, Li Hsu","doi":"10.1093/biomtc/ujae109","DOIUrl":"10.1093/biomtc/ujae109","url":null,"abstract":"Ancestry-specific proteome-wide association studies (PWAS) based on genetically predicted protein expression can reveal complex disease etiology specific to certain ancestral groups. These studies require ancestry-specific models for protein expression as a function of SNP genotypes. In order to improve protein expression prediction in ancestral populations historically underrepresented in genomic studies, we propose a new penalized maximum likelihood estimator for fitting ancestry-specific joint protein quantitative trait loci models. Our estimator borrows information across ancestral groups, while simultaneously allowing for heterogeneous error variances and regression coefficients. We propose an alternative parameterization of our model that makes the objective function convex and the penalty scale invariant. To improve computational efficiency, we propose an approximate version of our method and study its theoretical properties. Our method provides a substantial improvement in protein expression prediction accuracy in individuals of African ancestry, and in a downstream PWAS analysis, leads to the discovery of multiple associations between protein expression and blood lipid traits in the African ancestry population.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11492996/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457175","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 new robust approach for the polytomous logistic regression model based on Rényi's pseudodistances. 基于 Rényi 伪距的多项式逻辑回归模型新稳健方法。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae125

Elena Castilla

引用次数: 0