Biometrics最新文献_第2页

Causal effect estimation in survival analysis with high dimensional confounders. 具有高维度混杂因素的生存分析中的因果效应估计。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae110

Fei Jiang, Ge Zhao, Rosa Rodriguez-Monguio, Yanyuan Ma

{"title":"Causal effect estimation in survival analysis with high dimensional confounders.","authors":"Fei Jiang, Ge Zhao, Rosa Rodriguez-Monguio, Yanyuan Ma","doi":"10.1093/biomtc/ujae110","DOIUrl":"10.1093/biomtc/ujae110","url":null,"abstract":"With the ever advancing of modern technologies, it has become increasingly common that the number of collected confounders exceeds the number of subjects in a data set. However, matching based methods for estimating causal treatment effect in their original forms are not capable of handling high-dimensional confounders, and their various modified versions lack statistical support and valid inference tools. In this article, we propose a new approach for estimating causal treatment effect, defined as the difference of the restricted mean survival time (RMST) under different treatments in high-dimensional setting for survival data. We combine the factor model and the sufficient dimension reduction techniques to construct propensity score and prognostic score. Based on these scores, we develop a kernel based doubly robust estimator of the RMST difference. We demonstrate its link to matching and establish the consistency and asymptotic normality of the estimator. We illustrate our method by analyzing a dataset from a study aimed at comparing the effects of two alternative treatments on the RMST of patients with diffuse large B cell lymphoma.","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/PMC11472547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457172","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

Derivation of outcome-dependent dietary patterns for low-income women obtained from survey data using a supervised weighted overfitted latent class analysis. 利用监督加权过度拟合潜类分析法，从调查数据中得出低收入妇女依赖结果的饮食模式。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae122

Stephanie M Wu, Matthew R Williams, Terrance D Savitsky, Briana J K Stephenson

{"title":"Derivation of outcome-dependent dietary patterns for low-income women obtained from survey data using a supervised weighted overfitted latent class analysis.","authors":"Stephanie M Wu, Matthew R Williams, Terrance D Savitsky, Briana J K Stephenson","doi":"10.1093/biomtc/ujae122","DOIUrl":"10.1093/biomtc/ujae122","url":null,"abstract":"Poor diet quality is a key modifiable risk factor for hypertension and disproportionately impacts low-income women. Analyzing diet-driven hypertensive outcomes in this demographic is challenging due to the complexity of dietary data and selection bias when the data come from surveys, a main data source for understanding diet-disease relationships in understudied populations. Supervised Bayesian model-based clustering methods summarize dietary data into latent patterns that holistically capture relationships among foods and a known health outcome but do not sufficiently account for complex survey design. This leads to biased estimation and inference and lack of generalizability of the patterns. To address this, we propose a supervised weighted overfitted latent class analysis (SWOLCA) based on a Bayesian pseudo-likelihood approach that integrates sampling weights into an exposure-outcome model for discrete data. Our model adjusts for stratification, clustering, and informative sampling, and handles modifying effects via interaction terms within a Markov chain Monte Carlo Gibbs sampling algorithm. Simulation studies confirm that the SWOLCA model exhibits good performance in terms of bias, precision, and coverage. Using data from the National Health and Nutrition Examination Survey (2015-2018), we demonstrate the utility of our model by characterizing dietary patterns associated with hypertensive outcomes among low-income women in the United States.","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/PMC11518851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142520912","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

Wasserstein regression with empirical measures and density estimation for sparse data. 稀疏数据的瓦瑟斯坦回归与经验度量和密度估计。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae127

Yidong Zhou, Hans-Georg Müller

{"title":"Wasserstein regression with empirical measures and density estimation for sparse data.","authors":"Yidong Zhou, Hans-Georg Müller","doi":"10.1093/biomtc/ujae127","DOIUrl":"https://doi.org/10.1093/biomtc/ujae127","url":null,"abstract":"The problem of modeling the relationship between univariate distributions and one or more explanatory variables lately has found increasing interest. Existing approaches proceed by substituting proxy estimated distributions for the typically unknown response distributions. These estimates are obtained from available data but are problematic when for some of the distributions only few data are available. Such situations are common in practice and cannot be addressed with currently available approaches, especially when one aims at density estimates. We show how this and other problems associated with density estimation such as tuning parameter selection and bias issues can be side-stepped when covariates are available. We also introduce a novel version of distribution-response regression that is based on empirical measures. By avoiding the preprocessing step of recovering complete individual response distributions, the proposed approach is applicable when the sample size available for each distribution varies and especially when it is small for some of the distributions but large for others. In this case, one can still obtain consistent distribution estimates even for distributions with only few data by gaining strength across the entire sample of distributions, while traditional approaches where distributions or densities are estimated individually fail, since sparsely sampled densities cannot be consistently estimated. The proposed model is demonstrated to outperform existing approaches through simulations and Environmental Influences on Child Health Outcomes data.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142581081","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 formal goodness-of-fit test for spatial binary Markov random field models. 空间二元马尔可夫随机场模型的正式拟合优度检验。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae119

Eva Biswas, Andee Kaplan, Mark S Kaiser, Daniel J Nordman

引用次数: 0

Robust and flexible learning of a high-dimensional classification rule using auxiliary outcomes. 利用辅助结果稳健灵活地学习高维分类规则。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae144

Muxuan Liang, Jaeyoung Park, Qing Lu, Xiang Zhong

引用次数: 0

An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables.

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae141

Lara Maleyeff, Shirin Golchi, Erica E M Moodie, Marie Hudson

{"title":"An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables.","authors":"Lara Maleyeff, Shirin Golchi, Erica E M Moodie, Marie Hudson","doi":"10.1093/biomtc/ujae141","DOIUrl":"https://doi.org/10.1093/biomtc/ujae141","url":null,"abstract":"Precision medicine is transforming healthcare by offering tailored treatments that enhance patient outcomes and reduce costs. As our understanding of complex diseases improves, clinical trials increasingly aim to detect subgroups of patients with enhanced treatment effects. Biomarker-driven adaptive enrichment designs, which initially enroll a broad population and later restrict to treatment-sensitive patients, are gaining popularity. However, current practice often assumes either pre-trial knowledge of biomarkers or a simple, linear relationship between continuous markers and treatment effectiveness. Motivated by a trial studying rheumatoid arthritis treatment, we propose a Bayesian adaptive enrichment design to identify predictive variables from a larger set of candidate biomarkers. Our approach uses a flexible modeling framework where the effects of continuous biomarkers are represented using free knot B-splines. We then estimate key parameters by marginalizing over all possible variable combinations using Bayesian model averaging. At interim analyses, we assess whether a biomarker-defined subgroup has enhanced or reduced treatment effects, allowing for early termination for efficacy or futility and restricting future enrollment to treatment-sensitive patients. We consider both pre-categorized and continuous biomarkers, the latter potentially having complex, nonlinear relationships to the outcome and treatment effect. Through simulations, we derive the operating characteristics of our design and compare its performance to existing methods.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827260","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

Case-crossover designs and overdispersion with application to air pollution epidemiology. 病例交叉设计和过度分散在空气污染流行病学中的应用。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae117

Samuel Perreault, Gracia Y Dong, Alex Stringer, Hwashin Shin, Patrick E Brown

{"title":"Case-crossover designs and overdispersion with application to air pollution epidemiology.","authors":"Samuel Perreault, Gracia Y Dong, Alex Stringer, Hwashin Shin, Patrick E Brown","doi":"10.1093/biomtc/ujae117","DOIUrl":"https://doi.org/10.1093/biomtc/ujae117","url":null,"abstract":"Over the last three decades, case-crossover designs have found many applications in health sciences, especially in air pollution epidemiology. They are typically used, in combination with partial likelihood techniques, to define a conditional logistic model for the responses, usually health outcomes, conditional on the exposures. Despite the fact that conditional logistic models have been shown equivalent, in typical air pollution epidemiology setups, to specific instances of the well-known Poisson time series model, it is often claimed that they cannot allow for overdispersion. This paper clarifies the relationship between case-crossover designs, the models that ensue from their use, and overdispersion. In particular, we propose to relax the assumption of independence between individuals traditionally made in case-crossover analyses, in order to explicitly introduce overdispersion in the conditional logistic model. As we show, the resulting overdispersed conditional logistic model coincides with the overdispersed, conditional Poisson model, in the sense that their likelihoods are simple re-expressions of one another. We further provide the technical details of a Bayesian implementation of the proposed case-crossover model, which we use to demonstrate, by means of a large simulation study, that standard case-crossover models can lead to dramatically underestimated coverage probabilities, while the proposed models do not. We also perform an illustrative analysis of the association between air pollution and morbidity in Toronto, Canada, which shows that the proposed models are more robust than standard ones to outliers such as those associated with public holidays.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457171","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 hierarchical random effects state-space model for modeling brain activities from electroencephalogram data. 根据脑电图数据建立大脑活动模型的分层随机效应状态空间模型。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae130

Xingche Guo, Bin Yang, Ji Meng Loh, Qinxia Wang, Yuanjia Wang

{"title":"A hierarchical random effects state-space model for modeling brain activities from electroencephalogram data.","authors":"Xingche Guo, Bin Yang, Ji Meng Loh, Qinxia Wang, Yuanjia Wang","doi":"10.1093/biomtc/ujae130","DOIUrl":"10.1093/biomtc/ujae130","url":null,"abstract":"Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. Electroencephalogram (EEG) has shown promise as a source of potential biomarkers for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions. This paper proposes a novel random effects state-space model (RESSM) for analyzing large-scale multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects. We incorporate multi-level random effects for temporal dynamical and spatial mapping matrices and address non-stationarity so that the brain connectivity patterns can vary over time. The model is fitted under a Bayesian hierarchical model framework coupled with a Gibbs sampler. Compared to previous mixed-effects state-space models, we directly model high-dimensional random effects matrices of interest without structural constraints and tackle the challenge of identifiability. Through extensive simulation studies, we demonstrate that our approach yields valid estimation and inference. We apply RESSM to a multi-site clinical trial of major depressive disorder (MDD). Our analysis uncovers significant differences in resting-state brain temporal dynamics among MDD patients compared to healthy individuals. In addition, we show the subject-level EEG features derived from RESSM exhibit a superior predictive value for the heterogeneous treatment effect compared to the EEG frequency band power, suggesting the potential of EEG as a valuable biomarker for MDD.","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/PMC11540184/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142590082","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

An exploratory penalized regression to identify combined effects of temporal variables-application to agri-environmental issues. 用于确定时间变量综合效应的探索性惩罚回归--应用于农业环境问题。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae134

Bénedicte Fontez, Patrice Loisel, Thierry Simonneau, Nadine Hilgert

{"title":"An exploratory penalized regression to identify combined effects of temporal variables-application to agri-environmental issues.","authors":"Bénedicte Fontez, Patrice Loisel, Thierry Simonneau, Nadine Hilgert","doi":"10.1093/biomtc/ujae134","DOIUrl":"https://doi.org/10.1093/biomtc/ujae134","url":null,"abstract":"The development of sensors is opening new avenues in several fields of activity. Concerning agricultural crops, complex combinations of agri-environmental dynamics, such as soil and climate variables, are now commonly recorded. These new kinds of measurements are an opportunity to improve knowledge of the drivers of crop yield and crop quality at harvest. This involves renewing statistical approaches to account for the combined variations of these dynamic variables, here considered as temporal variables. The objective of the paper is to estimate an interpretable model to study the influence of the two combined inputs on a scalar output. A Sparse and Structured Procedure is proposed to Identify Combined Effects of Formatted temporal Predictors, hereafter denoted S piceFP. The method is based on the transformation of both temporal variables into categorical variables by defining joint modalities, from which a collection of multiple regression models is then derived. The regressors are the frequencies associated with joint class intervals. The class intervals and related regression coefficients are determined using a generalized fused lasso. S piceFP is a generic and exploratory approach. The simulations we performed show that it is flexible enough to select the non-null or influential modalities of values. A motivating example for grape quality is presented.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692652","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

Debiased high-dimensional regression calibration for errors-in-variables log-contrast models.

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae153

Huali Zhao, Tianying Wang

引用次数: 0