Biometrics最新文献_第5页

A mixed-effects Bayesian regression model for multivariate group testing data. 多变量组检验数据的混合效应贝叶斯回归模型。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf028

Christopher S McMahan, Chase N Joyner, Joshua M Tebbs, Christopher R Bilder

{"title":"A mixed-effects Bayesian regression model for multivariate group testing data.","authors":"Christopher S McMahan, Chase N Joyner, Joshua M Tebbs, Christopher R Bilder","doi":"10.1093/biomtc/ujaf028","DOIUrl":"10.1093/biomtc/ujaf028","url":null,"abstract":"Laboratories use group (pooled) testing with multiplex assays to reduce the time and cost associated with screening large populations for infectious diseases. Multiplex assays test for multiple diseases simultaneously, and combining their use with group testing can lead to highly efficient screening protocols. However, these benefits come at the expense of a more complex data structure which can hinder surveillance efforts. To overcome this challenge, we develop a general Bayesian framework to estimate a mixed multivariate probit model with data arising from any group testing protocol that uses multiplex assays. In the formulation of this model, we account for the correlation between true disease statuses and heterogeneity across population subgroups, and we provide for automated variable selection through the adoption of spike and slab priors. To perform model fitting, we develop an attractive posterior sampling algorithm which is straightforward to implement. We illustrate our methodology through numerical studies and analyze chlamydia and gonorrhea group testing data collected by the State Hygienic Laboratory at the University of Iowa.","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/PMC11926587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673245","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

Improving estimation efficiency for survival data analysis by integrating a coarsened time-to-event outcome from an external study. 通过整合来自外部研究的粗化时间到事件结果，提高生存数据分析的估计效率。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae168

Daxuan Deng, Lijun Zhang, Hao Feng, Vernon M Chinchilli, Chixiang Chen, Ming Wang

{"title":"Improving estimation efficiency for survival data analysis by integrating a coarsened time-to-event outcome from an external study.","authors":"Daxuan Deng, Lijun Zhang, Hao Feng, Vernon M Chinchilli, Chixiang Chen, Ming Wang","doi":"10.1093/biomtc/ujae168","DOIUrl":"10.1093/biomtc/ujae168","url":null,"abstract":"In the era of big data, increasing availability of data makes combining different data sources to obtain more accurate estimations a popular topic. However, the development of data integration is often hindered by the heterogeneity in data forms across studies. In this paper, we focus on a case in survival analysis where we have primary study data with a continuous time-to-event outcome and complete covariate measurements, while the data from an external study contain an outcome observed at regular intervals, and only a subset of covariates is measured. To incorporate external information while accounting for the different data forms, we posit working models and obtain informative weights by empirical likelihood, which will be used to construct a weighted estimator in the main analysis. We have established the theory demonstrating that the new estimator has higher estimation efficiency compared to the conventional ones, and this advantage is robust to working model misspecification, as confirmed in our simulation studies. To assess its utility, we apply our method to accommodate data from the National Alzheimer's Coordinating Center to improve the analysis of the Alzheimer's Disease Neuroimaging Initiative Phase 1 study.","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/PMC11747882/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142999230","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

Pseudo-observations for bivariate survival data. 双变量生存数据的伪观察。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf006

Yael Travis-Lumer, Micha Mandel, Rebecca A Betensky

{"title":"Pseudo-observations for bivariate survival data.","authors":"Yael Travis-Lumer, Micha Mandel, Rebecca A Betensky","doi":"10.1093/biomtc/ujaf006","DOIUrl":"10.1093/biomtc/ujaf006","url":null,"abstract":"The pseudo-observations approach has been gaining popularity as a method to estimate covariate effects on censored survival data. It is used regularly to estimate covariate effects on quantities such as survival probabilities, restricted mean life, cumulative incidence, and others. In this work, we propose to generalize the pseudo-observations approach to situations where a bivariate failure-time variable is observed, subject to right censoring. The idea is to first estimate the joint survival function of both failure times and then use it to define the relevant pseudo-observations. Once the pseudo-observations are calculated, they are used as the response in a generalized linear model. We consider 2 common nonparametric estimators of the joint survival function: the estimator of Lin and Ying (1993) and the Dabrowska estimator (Dabrowska, 1988). For both estimators, we show that our bivariate pseudo-observations approach produces regression estimates that are consistent and asymptotically normal. Our proposed method enables estimation of covariate effects on quantities such as the joint survival probability at a fixed bivariate time point or simultaneously at several time points and, consequentially, can estimate covariate-adjusted conditional survival probabilities. We demonstrate the method using simulations and an analysis of 2 real-world datasets.","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":"143188046","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

Addressing selection bias in cluster randomized experiments via weighting. 通过加权处理聚类随机实验中的选择偏差。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf013

Georgia Papadogeorgou, Bo Liu, Fan Li, Fan Li

{"title":"Addressing selection bias in cluster randomized experiments via weighting.","authors":"Georgia Papadogeorgou, Bo Liu, Fan Li, Fan Li","doi":"10.1093/biomtc/ujaf013","DOIUrl":"10.1093/biomtc/ujaf013","url":null,"abstract":"In cluster randomized experiments, individuals are often recruited after the cluster treatment assignment, and data are typically only available for the recruited sample. Post-randomization recruitment can lead to selection bias, inducing systematic differences between the overall and the recruited populations and between the recruited intervention and control arms. In this setting, we define causal estimands for the overall and the recruited populations. We prove, under the assumption of ignorable recruitment, that the average treatment effect on the recruited population can be consistently estimated from the recruited sample using inverse probability weighting. Generally, we cannot identify the average treatment effect on the overall population. Nonetheless, we show, via a principal stratification formulation, that one can use weighting of the recruited sample to identify treatment effects on two meaningful subpopulations of the overall population: Individuals who would be recruited into the study regardless of the assignment, and individuals who would be recruited into the study under treatment but not under control. We develop an estimation strategy and a sensitivity analysis approach for checking the ignorable recruitment assumption, which we implement in the publicly available CRTrecruit R package. The proposed methods are applied to the ARTEMIS cluster randomized trial, where removing co-payment barriers increases the persistence of P2Y$_{12}$ inhibitor among the always-recruited population.","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":"143571960","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

Distributed lag models for retrospective cohort data with application to a study of built environment and body weight. 回顾性队列数据的分布滞后模型及其在建筑环境和体重研究中的应用。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae166

Jennifer F Bobb, Stephen J Mooney, Maricela Cruz, Anne Vernez Moudon, Adam Drewnowski, David Arterburn, Andrea J Cook

{"title":"Distributed lag models for retrospective cohort data with application to a study of built environment and body weight.","authors":"Jennifer F Bobb, Stephen J Mooney, Maricela Cruz, Anne Vernez Moudon, Adam Drewnowski, David Arterburn, Andrea J Cook","doi":"10.1093/biomtc/ujae166","DOIUrl":"10.1093/biomtc/ujae166","url":null,"abstract":"Distributed lag models (DLMs) estimate the health effects of exposure over multiple time lags prior to the outcome and are widely used in time series studies. Applying DLMs to retrospective cohort studies is challenging due to inconsistent lengths of exposure history across participants, which is common when using electronic health record databases. A standard approach is to define subcohorts of individuals with some minimum exposure history, but this limits power and may amplify selection bias. We propose alternative full-cohort methods that use all available data while simultaneously enabling examination of the longest time lag estimable in the cohort. Through simulation studies, we find that restricting to a subcohort can lead to biased estimates of exposure effects due to confounding by correlated exposures at more distant lags. By contrast, full-cohort methods that incorporate multiple imputation of complete exposure histories can avoid this bias to efficiently estimate lagged and cumulative effects. Applying full-cohort DLMs to a study examining the association between residential density (a proxy for walkability) over 12 years and body weight, we find evidence of an immediate effect in the prior 1-2 years. We also observed an association at the maximal lag considered (12 years prior), which we posit reflects an earlier ($ge$12 years) or incrementally increasing prior effect over time. DLMs can be efficiently incorporated within retrospective cohort studies to identify critical windows of exposure.","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/PMC11760659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143031922","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

Distributed model building and recursive integration for big spatial data modeling. 大空间数据建模的分布式模型构建与递归集成。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae159

Emily C Hector, Brian J Reich, Ani Eloyan

引用次数: 0

A simple and powerful method for large-scale composite null hypothesis testing with applications in mediation analysis. 一种简单而有效的大规模复合零假设检验方法及其在中介分析中的应用。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf011

Yaowu Liu

引用次数: 0

The subtype-free average causal effect for heterogeneous disease etiology. 异质性疾病病因的无亚型平均因果效应。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf016

A Sasson, M Wang, S Ogino, D Nevo

{"title":"The subtype-free average causal effect for heterogeneous disease etiology.","authors":"A Sasson, M Wang, S Ogino, D Nevo","doi":"10.1093/biomtc/ujaf016","DOIUrl":"10.1093/biomtc/ujaf016","url":null,"abstract":"Studies have shown that the effect an exposure may have on a disease can vary for different subtypes of the same disease. However, existing approaches to estimate and compare these effects largely overlook causality. In this paper, we study the effect smoking may have on having colorectal cancer subtypes defined by a trait known as microsatellite instability (MSI). We use principal stratification to propose an alternative causal estimand, the Subtype-Free Average Causal Effect (SF-ACE). The SF-ACE is the causal effect of the exposure among those who would be free from other disease subtypes under any exposure level. We study non-parametric identification of the SF-ACE and discuss different monotonicity assumptions, which are more nuanced than in the standard setting. As is often the case with principal stratum effects, the assumptions underlying the identification of the SF-ACE from the data are untestable and can be too strong. Therefore, we also develop sensitivity analysis methods that relax these assumptions. We present 3 different estimators, including a doubly robust estimator, for the SF-ACE. We implement our methodology for data from 2 large cohorts to study the heterogeneity in the causal effect of smoking on colorectal cancer with respect to MSI subtypes.","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/PMC11848129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482137","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

Statistical inference on change points in generalized semiparametric segmented models. 广义半参数分段模型中变化点的统计推断。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf022

Guangyu Yang, Baqun Zhang, Min Zhang

{"title":"Statistical inference on change points in generalized semiparametric segmented models.","authors":"Guangyu Yang, Baqun Zhang, Min Zhang","doi":"10.1093/biomtc/ujaf022","DOIUrl":"10.1093/biomtc/ujaf022","url":null,"abstract":"The segmented model has significant applications in scientific research when the change-point effect exists. In this article, we propose a comprehensive semiparametric framework in segmented models to test the existence and estimate the location of change points in the generalized outcome setting. The proposed framework is based on a semismooth estimating equation for the change-point estimation and an average score-type test for hypothesis testing. The root-n consistency, asymptotic normality, and asymptotic efficiency of estimators for all parameters in the segmented model are rigorously studied. The distribution of the average score-type test statistics under the null hypothesis is rigorously derived. Extensive simulation studies are conducted to assess the numerical performance of the proposed change-point estimation method and the average score-type test. We investigate change-point effects of baseline glomerular filtration rate and body mass index on bleeding after intervention using data from Blue Cross Blue Shield. This application study successfully identifies statistically significant change-point effects, with the estimated values providing clinically meaningful insights.","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":"143603115","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 model-free framework for evaluating the reliability of a new device with multiple imperfect reference standards. 具有多个不完善参考标准的新设备可靠性评估的无模型框架。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf025

Ying Cui, Qi Yu, Amita Manatunga, Jeong Hoon Jang

{"title":"A model-free framework for evaluating the reliability of a new device with multiple imperfect reference standards.","authors":"Ying Cui, Qi Yu, Amita Manatunga, Jeong Hoon Jang","doi":"10.1093/biomtc/ujaf025","DOIUrl":"10.1093/biomtc/ujaf025","url":null,"abstract":"A common practice for establishing the reliability of a new computer-aided diagnostic (CAD) device is to evaluate how well its clinical measurements agree with those of a gold standard test. However, in many clinical studies, a gold standard is unavailable, and one needs to aggregate information from multiple imperfect reference standards for evaluation. A key challenge here is the heterogeneity in diagnostic accuracy across different reference standards, which may lead to biased evaluation of a device if improperly accounted for during the aggregation process. We propose an intuitive and easy-to-use statistical framework for evaluation of a device by assessing agreement between its measurements and the weighted sum of measurements from multiple imperfect reference standards, where weights representing relative reliability of each reference standard are determined by a model-free, unsupervised inductive procedure. Specifically, the inductive procedure recursively assigns higher weights to reference standards whose assessments are more consistent with each other and form a majority opinion, while assigning lower weights to those with greater discrepancies. Unlike existing methods, our approach does not require any modeling assumptions or external data to quantify heterogeneous accuracy levels of reference standards. It only requires specifying an appropriate agreement index used for weight assignment and device evaluation. The framework is applied to evaluate a CAD device for kidney obstruction by comparing its diagnostic ratings with those of multiple nuclear medicine physicians.","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/PMC11911720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647105","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