Biometrics最新文献_第5页

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

A multivariate Polya tree model for meta-analysis with event-time distributions. 用于事件时间分布荟萃分析的多元波利亚树模型

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae136

Giovanni Poli, Elena Fountzilas, Apostolia-Maria Tsimeridou, Peter Müller

{"title":"A multivariate Polya tree model for meta-analysis with event-time distributions.","authors":"Giovanni Poli, Elena Fountzilas, Apostolia-Maria Tsimeridou, Peter Müller","doi":"10.1093/biomtc/ujae136","DOIUrl":"10.1093/biomtc/ujae136","url":null,"abstract":"We develop a nonparametric Bayesian prior for a family of random probability measures by extending the Polya tree ($mbox{PT}$) prior to a joint prior for a set of probability measures $G_1,dots ,G_n$, suitable for meta-analysis with event-time outcomes. In the application to meta-analysis, $G_i$ is the event-time distribution specific to study $i$. The proposed model defines a regression on study-specific covariates by introducing increased correlation for any pair of studies with similar characteristics. The desired multivariate $mbox{PT}$ model is constructed by introducing a hierarchical prior on the conditional splitting probabilities in the $mbox{PT}$ construction for each of the $G_i$. The hierarchical prior replaces the independent beta priors for the splitting probability in the PT construction with a Gaussian process prior for corresponding (logit) splitting probabilities across all studies. The Gaussian process is indexed by study-specific covariates, introducing the desired dependence with increased correlation for similar studies. The main feature of the proposed construction is (conditionally) conjugate posterior updating with commonly reported inference summaries for event-time data. The construction is motivated by a meta-analysis over cancer immunotherapy studies.","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":"142827258","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 causal inference framework for leveraging external controls in hybrid trials. 在混合试验中利用外部控制的因果推理框架。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae095

Michael Valancius, Herbert Pang, Jiawen Zhu, Stephen R Cole, Michele Jonsson Funk, Michael R Kosorok

{"title":"A causal inference framework for leveraging external controls in hybrid trials.","authors":"Michael Valancius, Herbert Pang, Jiawen Zhu, Stephen R Cole, Michele Jonsson Funk, Michael R Kosorok","doi":"10.1093/biomtc/ujae095","DOIUrl":"10.1093/biomtc/ujae095","url":null,"abstract":"We consider the challenges associated with causal inference in settings where data from a randomized trial are augmented with control data from an external source to improve efficiency in estimating the average treatment effect (ATE). This question is motivated by the SUNFISH trial, which investigated the effect of risdiplam on motor function in patients with spinal muscular atrophy. While the original analysis used only data generated by the trial, we explore an alternative analysis incorporating external controls from the placebo arm of a historical trial. We cast the setting into a formal causal inference framework and show how these designs are characterized by a lack of full randomization to treatment and heightened dependency on modeling. To address this, we outline sufficient causal assumptions about the exchangeability between the internal and external controls to identify the ATE and establish a connection with novel graphical criteria. Furthermore, we propose estimators, review efficiency bounds, develop an approach for efficient doubly robust estimation even when unknown nuisance models are estimated with flexible machine learning methods, suggest model diagnostics, and demonstrate finite-sample performance of the methods through a simulation study. The ideas and methods are illustrated through their application to the SUNFISH trial, where we find that external controls can increase the efficiency of treatment effect estimation.","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/PMC11546536/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142602843","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

De-biasing the bias: methods for improving disparity assessments with noisy group measurements. 消除偏差：用噪声组测量改进差异评估的方法。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae155

Solvejg Wastvedt, Joshua Snoke, Denis Agniel, Julie Lai, Marc N Elliott, Steven C Martino

{"title":"De-biasing the bias: methods for improving disparity assessments with noisy group measurements.","authors":"Solvejg Wastvedt, Joshua Snoke, Denis Agniel, Julie Lai, Marc N Elliott, Steven C Martino","doi":"10.1093/biomtc/ujae155","DOIUrl":"10.1093/biomtc/ujae155","url":null,"abstract":"Health care decisions are increasingly informed by clinical decision support algorithms, but these algorithms may perpetuate or increase racial and ethnic disparities in access to and quality of health care. Further complicating the problem, clinical data often have missing or poor quality racial and ethnic information, which can lead to misleading assessments of algorithmic bias. We present novel statistical methods that allow for the use of probabilities of racial/ethnic group membership in assessments of algorithm performance and quantify the statistical bias that results from error in these imputed group probabilities. We propose a sensitivity analysis approach to estimating the statistical bias that allows practitioners to assess disparities in algorithm performance under a range of assumed levels of group probability error. We also prove theoretical bounds on the statistical bias for a set of commonly used fairness metrics and describe real-world scenarios where our theoretical results are likely to apply. We present a case study using imputed race and ethnicity from the modified Bayesian Improved First and Surname Geocoding algorithm for estimation of disparities in a clinical decision support algorithm used to inform osteoporosis treatment. Our novel methods allow policymakers to understand the range of potential disparities under a given algorithm even when race and ethnicity information is missing and to make informed decisions regarding the implementation of machine learning for clinical decision support.","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":"142891791","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 Bayesian framework for causal analysis of recurrent events with timing misalignment. 对具有时间错位的重复事件进行因果分析的贝叶斯框架。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae145

Arman Oganisian, Anthony Girard, Jon A Steingrimsson, Patience Moyo

{"title":"A Bayesian framework for causal analysis of recurrent events with timing misalignment.","authors":"Arman Oganisian, Anthony Girard, Jon A Steingrimsson, Patience Moyo","doi":"10.1093/biomtc/ujae145","DOIUrl":"10.1093/biomtc/ujae145","url":null,"abstract":"Observational studies of recurrent event rates are common in biomedical statistics. Broadly, the goal is to estimate differences in event rates under 2 treatments within a defined target population over a specified follow-up window. Estimation with observational data is challenging because, while membership in the target population is defined in terms of eligibility criteria, treatment is rarely observed exactly at the time of eligibility. Ad hoc solutions to this timing misalignment can induce bias by incorrectly attributing prior event counts and person-time to treatment. Even if eligibility and treatment are aligned, a terminal event process (eg, death) often stops the recurrent event process of interest. In practice, both processes can be censored so that events are not observed over the entire follow-up window. Our approach addresses misalignment by casting it as a time-varying treatment problem: some patients are on treatment at eligibility while others are off treatment but may switch to treatment at a specified time-if they survive long enough. We define and identify an average causal effect estimand under right-censoring. Estimation is done using a g-computation procedure with a joint semiparametric Bayesian model for the death and recurrent event processes. We apply the method to contrast hospitalization rates among patients with different opioid treatments using Medicare insurance claims 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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827256","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 Bayesian joint model for mediation analysis with matrix-valued mediators. 矩阵值中介分析的贝叶斯联合模型。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae143

Zijin Liu, Zhihui Amy Liu, Ali Hosni, John Kim, Bei Jiang, Olli Saarela

{"title":"A Bayesian joint model for mediation analysis with matrix-valued mediators.","authors":"Zijin Liu, Zhihui Amy Liu, Ali Hosni, John Kim, Bei Jiang, Olli Saarela","doi":"10.1093/biomtc/ujae143","DOIUrl":"https://doi.org/10.1093/biomtc/ujae143","url":null,"abstract":"Unscheduled treatment interruptions may lead to reduced quality of care in radiation therapy (RT). Identifying the RT prescription dose effects on the outcome of treatment interruptions, mediated through doses distributed into different organs at risk (OARs), can inform future treatment planning. The radiation exposure to OARs can be summarized by a matrix of dose-volume histograms (DVH) for each patient. Although various methods for high-dimensional mediation analysis have been proposed recently, few studies investigated how matrix-valued data can be treated as mediators. In this paper, we propose a novel Bayesian joint mediation model for high-dimensional matrix-valued mediators. In this joint model, latent features are extracted from the matrix-valued data through an adaptation of probabilistic multilinear principal components analysis (MPCA), retaining the inherent matrix structure. We derive and implement a Gibbs sampling algorithm to jointly estimate all model parameters, and introduce a Varimax rotation method to identify active indicators of mediation among the matrix-valued data. Our simulation study finds that the proposed joint model has higher efficiency in estimating causal decomposition effects compared to an alternative two-step method, and demonstrates that the mediation effects can be identified and visualized in the matrix form. We apply the method to study the effect of prescription dose on treatment interruptions in anal canal cancer patients.","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":"142821775","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

Spatially adaptive variable screening in presurgical functional magnetic resonance imaging data analysis. 术前功能磁共振成像数据分析中的空间自适应变量筛选。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae157

Yifei Hu, Xinge Jessie Jeng

{"title":"Spatially adaptive variable screening in presurgical functional magnetic resonance imaging data analysis.","authors":"Yifei Hu, Xinge Jessie Jeng","doi":"10.1093/biomtc/ujae157","DOIUrl":"https://doi.org/10.1093/biomtc/ujae157","url":null,"abstract":"Accurate delineation of functional brain regions adjacent to tumors is imperative for planning neurosurgery that preserves critical functions. Functional magnetic resonance imaging (fMRI) plays an increasingly pivotal role in presurgical counseling and planning. In the analysis of presurgical fMRI data, the impact of false negatives on patients surpasses that of false positives because failure to identify functional regions and unintentionally resecting critical tissues can result in severe harm to patients. This paper introduces a novel metric, the Bayesian missed discovery rate (BMDR), designed for controlling false negatives within the voxel-specific mixture model. Building on the BMDR metric, we propose a new variable screening procedure that not only ensures effective control of false negatives but also capitalizes on the spatial structure of fMRI data. In comparison to existing statistical methods in fMRI data analysis, our new procedure directly regulates false negatives at a desirable level and is entirely data-driven. Moreover, it significantly differs from current false-negative control procedures by incorporating spatial information. Numerical examples demonstrate that the new method outperforms several state-of-the-art methods in retaining signal voxels, particularly the subtle ones at the boundaries of functional regions, while achieving a cleaner separation of functional regions from background noise. These findings hold promising implications for planning function-preserving neurosurgery.","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":"142920669","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