Biostatistics最新文献_第4页

Estimating causal effects for binary outcomes using per-decision inverse probability weighting. 使用每次决定的反概率加权法估算二元结果的因果效应。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae025

Yihan Bao, Lauren Bell, Elizabeth Williamson, Claire Garnett, Tianchen Qian

{"title":"Estimating causal effects for binary outcomes using per-decision inverse probability weighting.","authors":"Yihan Bao, Lauren Bell, Elizabeth Williamson, Claire Garnett, Tianchen Qian","doi":"10.1093/biostatistics/kxae025","DOIUrl":"10.1093/biostatistics/kxae025","url":null,"abstract":"Micro-randomized trials are commonly conducted for optimizing mobile health interventions such as push notifications for behavior change. In analyzing such trials, causal excursion effects are often of primary interest, and their estimation typically involves inverse probability weighting (IPW). However, in a micro-randomized trial, additional treatments can often occur during the time window over which an outcome is defined, and this can greatly inflate the variance of the causal effect estimator because IPW would involve a product of numerous weights. To reduce variance and improve estimation efficiency, we propose two new estimators using a modified version of IPW, which we call \"per-decision IPW.\" The second estimator further improves efficiency using the projection idea from the semiparametric efficiency theory. These estimators are applicable when the outcome is binary and can be expressed as the maximum of a series of sub-outcomes defined over sub-intervals of time. We establish the estimators' consistency and asymptotic normality. Through simulation studies and real data applications, we demonstrate substantial efficiency improvement of the proposed estimator over existing estimators. The new estimators can be used to improve the precision of primary and secondary analyses for micro-randomized trials with binary outcomes.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141794123","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 thresholded modeling for integrating brain node and network predictors. 脑节点和网络预测器集成的贝叶斯阈值建模。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae048

Zhe Sun, Wanwan Xu, Tianxi Li, Jian Kang, Gregorio Alanis-Lobato, Yize Zhao

{"title":"Bayesian thresholded modeling for integrating brain node and network predictors.","authors":"Zhe Sun, Wanwan Xu, Tianxi Li, Jian Kang, Gregorio Alanis-Lobato, Yize Zhao","doi":"10.1093/biostatistics/kxae048","DOIUrl":"10.1093/biostatistics/kxae048","url":null,"abstract":"Progress in neuroscience has provided unprecedented opportunities to advance our understanding of brain alterations and their correspondence to phenotypic profiles. With data collected from various imaging techniques, studies have integrated different types of information ranging from brain structure, function, or metabolism. More recently, an emerging way to categorize imaging traits is through a metric hierarchy, including localized node-level measurements and interactive network-level metrics. However, limited research has been conducted to integrate these different hierarchies and achieve a better understanding of the neurobiological mechanisms and communications. In this work, we address this literature gap by proposing a Bayesian regression model under both vector-variate and matrix-variate predictors. To characterize the interplay between different predicting components, we propose a set of biologically plausible prior models centered on an innovative joint thresholded prior. This captures the coupling and grouping effect of signal patterns, as well as their spatial contiguity across brain anatomy. By developing a posterior inference, we can identify and quantify the uncertainty of signaling node- and network-level neuromarkers, as well as their predictive mechanism for phenotypic outcomes. Through extensive simulations, we demonstrate that our proposed method outperforms the alternative approaches substantially in both out-of-sample prediction and feature selection. By implementing the model to study children's general mental abilities, we establish a powerful predictive mechanism based on the identified task contrast traits and resting-state sub-networks.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823287/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142959273","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 semiparametric Gaussian mixture model for chest CT-based 3D blood vessel reconstruction. 基于胸部 CT 的三维血管重建半参数高斯混合物模型

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae013

Qianhan Zeng, Jing Zhou, Ying Ji, Hansheng Wang

{"title":"A semiparametric Gaussian mixture model for chest CT-based 3D blood vessel reconstruction.","authors":"Qianhan Zeng, Jing Zhou, Ying Ji, Hansheng Wang","doi":"10.1093/biostatistics/kxae013","DOIUrl":"10.1093/biostatistics/kxae013","url":null,"abstract":"Computed tomography (CT) has been a powerful diagnostic tool since its emergence in the 1970s. Using CT data, 3D structures of human internal organs and tissues, such as blood vessels, can be reconstructed using professional software. This 3D reconstruction is crucial for surgical operations and can serve as a vivid medical teaching example. However, traditional 3D reconstruction heavily relies on manual operations, which are time-consuming, subjective, and require substantial experience. To address this problem, we develop a novel semiparametric Gaussian mixture model tailored for the 3D reconstruction of blood vessels. This model extends the classical Gaussian mixture model by enabling nonparametric variations in the component-wise parameters of interest according to voxel positions. We develop a kernel-based expectation-maximization algorithm for estimating the model parameters, accompanied by a supporting asymptotic theory. Furthermore, we propose a novel regression method for optimal bandwidth selection. Compared to the conventional cross-validation-based (CV) method, the regression method outperforms the CV method in terms of computational and statistical efficiency. In application, this methodology facilitates the fully automated reconstruction of 3D blood vessel structures with remarkable accuracy.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140869271","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

Exposure proximal immune correlates analysis. 接触近端免疫相关性分析。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae031

Ying Huang, Dean Follmann

{"title":"Exposure proximal immune correlates analysis.","authors":"Ying Huang, Dean Follmann","doi":"10.1093/biostatistics/kxae031","DOIUrl":"10.1093/biostatistics/kxae031","url":null,"abstract":"Immune response decays over time, and vaccine-induced protection often wanes. Understanding how vaccine efficacy changes over time is critical to guiding the development and application of vaccines in preventing infectious diseases. The objective of this article is to develop statistical methods that assess the effect of decaying immune responses on the risk of disease and on vaccine efficacy, within the context of Cox regression with sparse sampling of immune responses, in a baseline-naive population. We aim to further disentangle the various aspects of the time-varying vaccine effect, whether direct on disease or mediated through immune responses. Based on time-to-event data from a vaccine efficacy trial and sparse sampling of longitudinal immune responses, we propose a weighted estimated induced likelihood approach that models the longitudinal immune response trajectory and the time to event separately. This approach assesses the effects of the decaying immune response, the peak immune response, and/or the waning vaccine effect on the risk of disease. The proposed method is applicable not only to standard randomized trial designs but also to augmented vaccine trial designs that re-vaccinate uninfected placebo recipients at the end of the standard trial period. We conducted simulation studies to evaluate the performance of our method and applied the method to analyze immune correlates from a phase III SARS-CoV-2 vaccine trial.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141984050","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

Correction. 更正。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae029

引用次数: 0

HMM for discovering decision-making dynamics using reinforcement learning experiments. 利用强化学习实验发现决策动态的 HMM。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae033

Xingche Guo, Donglin Zeng, Yuanjia Wang

{"title":"HMM for discovering decision-making dynamics using reinforcement learning experiments.","authors":"Xingche Guo, Donglin Zeng, Yuanjia Wang","doi":"10.1093/biostatistics/kxae033","DOIUrl":"10.1093/biostatistics/kxae033","url":null,"abstract":"Major depressive disorder (MDD), a leading cause of years of life lived with disability, presents challenges in diagnosis and treatment due to its complex and heterogeneous nature. Emerging evidence indicates that reward processing abnormalities may serve as a behavioral marker for MDD. To measure reward processing, patients perform computer-based behavioral tasks that involve making choices or responding to stimulants that are associated with different outcomes, such as gains or losses in the laboratory. Reinforcement learning (RL) models are fitted to extract parameters that measure various aspects of reward processing (e.g. reward sensitivity) to characterize how patients make decisions in behavioral tasks. Recent findings suggest the inadequacy of characterizing reward learning solely based on a single RL model; instead, there may be a switching of decision-making processes between multiple strategies. An important scientific question is how the dynamics of strategies in decision-making affect the reward learning ability of individuals with MDD. Motivated by the probabilistic reward task within the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel RL-HMM (hidden Markov model) framework for analyzing reward-based decision-making. Our model accommodates decision-making strategy switching between two distinct approaches under an HMM: subjects making decisions based on the RL model or opting for random choices. We account for continuous RL state space and allow time-varying transition probabilities in the HMM. We introduce a computationally efficient Expectation-maximization (EM) algorithm for parameter estimation and use a nonparametric bootstrap for inference. Extensive simulation studies validate the finite-sample performance of our method. We apply our approach to the EMBARC study to show that MDD patients are less engaged in RL compared to the healthy controls, and engagement is associated with brain activities in the negative affect circuitry during an emotional conflict task.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142127451","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

Covariate-adjusted estimators of diagnostic accuracy in randomized trials.

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxaf005

Jon A Steingrimsson

{"title":"Covariate-adjusted estimators of diagnostic accuracy in randomized trials.","authors":"Jon A Steingrimsson","doi":"10.1093/biostatistics/kxaf005","DOIUrl":"https://doi.org/10.1093/biostatistics/kxaf005","url":null,"abstract":"Randomized controlled trials evaluating the diagnostic accuracy of a marker frequently collect information on baseline covariates in addition to information on the marker and the reference standard. However, standard estimators of sensitivity and specificity do not use data on baseline covariates and restrict the analysis to data from participants with a positive reference standard in the intervention arm being evaluated. Covariate-adjusted estimators for marginal treatment effects have been developed and been advocated for by regulatory agencies because they can improve power compared to unadjusted estimators. Despite this, similar covariate-adjusted estimators for marginal sensitivity and specificity have not yet been developed. In this manuscript, we address this gap by developing covariate-adjusted estimators for marginal sensitivity and specificity of a diagnostic test that leverage baseline covariate information. The estimators also use data from all participants, not just participants with a positive reference standard in the intervention arm being evaluated. We derive the asymptotic properties of the estimators and evaluate the finite sample properties of the estimators using simulations and by analyzing data on lung cancer screening.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626881","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

Simultaneous clustering and estimation of networks in multiple graphical models. 在多个图形模型中同时对网络进行聚类和估算。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae015

Gen Li, Miaoyan Wang

{"title":"Simultaneous clustering and estimation of networks in multiple graphical models.","authors":"Gen Li, Miaoyan Wang","doi":"10.1093/biostatistics/kxae015","DOIUrl":"10.1093/biostatistics/kxae015","url":null,"abstract":"Gaussian graphical models are widely used to study the dependence structure among variables. When samples are obtained from multiple conditions or populations, joint analysis of multiple graphical models are desired due to their capacity to borrow strength across populations. Nonetheless, existing methods often overlook the varying levels of similarity between populations, leading to unsatisfactory results. Moreover, in many applications, learning the population-level clustering structure itself is of particular interest. In this article, we develop a novel method, called Simultaneous Clustering and Estimation of Networks via Tensor decomposition (SCENT), that simultaneously clusters and estimates graphical models from multiple populations. Precision matrices from different populations are uniquely organized as a three-way tensor array, and a low-rank sparse model is proposed for joint population clustering and network estimation. We develop a penalized likelihood method and an augmented Lagrangian algorithm for model fitting. We also establish the clustering accuracy and norm consistency of the estimated precision matrices. We demonstrate the efficacy of the proposed method with comprehensive simulation studies. The application to the Genotype-Tissue Expression multi-tissue gene expression data provides important insights into tissue clustering and gene coexpression patterns in multiple brain tissues.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11826093/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141263584","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

BAMITA: Bayesian multiple imputation for tensor arrays. BAMITA：张量阵列的贝叶斯多重估算。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae047

Ziren Jiang, Gen Li, Eric F Lock

{"title":"BAMITA: Bayesian multiple imputation for tensor arrays.","authors":"Ziren Jiang, Gen Li, Eric F Lock","doi":"10.1093/biostatistics/kxae047","DOIUrl":"10.1093/biostatistics/kxae047","url":null,"abstract":"Data increasingly take the form of a multi-way array, or tensor, in several biomedical domains. Such tensors are often incompletely observed. For example, we are motivated by longitudinal microbiome studies in which several timepoints are missing for several subjects. There is a growing literature on missing data imputation for tensors. However, existing methods give a point estimate for missing values without capturing uncertainty. We propose a multiple imputation approach for tensors in a flexible Bayesian framework, that yields realistic simulated values for missing entries and can propagate uncertainty through subsequent analyses. Our model uses efficient and widely applicable conjugate priors for a CANDECOMP/PARAFAC (CP) factorization, with a separable residual covariance structure. This approach is shown to perform well with respect to both imputation accuracy and uncertainty calibration, for scenarios in which either single entries or entire fibers of the tensor are missing. For two microbiome applications, it is shown to accurately capture uncertainty in the full microbiome profile at missing timepoints and used to infer trends in species diversity for the population. Documented R code to perform our multiple imputation approach is available at https://github.com/lockEF/MultiwayImputation.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823239/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824682","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

Selection processes, transportability, and failure time analysis in life history studies. 生命史研究中的选择过程、可迁移性和失效时间分析。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae039

Richard J Cook, Jerald F Lawless

{"title":"Selection processes, transportability, and failure time analysis in life history studies.","authors":"Richard J Cook, Jerald F Lawless","doi":"10.1093/biostatistics/kxae039","DOIUrl":"10.1093/biostatistics/kxae039","url":null,"abstract":"In life history analysis of data from cohort studies, it is important to address the process by which participants are identified and selected. Many health studies select or enrol individuals based on whether they have experienced certain health related events, for example, disease diagnosis or some complication from disease. Standard methods of analysis rely on assumptions concerning the independence of selection and a person's prospective life history process, given their prior history. Violations of such assumptions are common, however, and can bias estimation of process features. This has implications for the internal and external validity of cohort studies, and for the transportabilty of results to a population. In this paper, we study failure time analysis by proposing a joint model for the cohort selection process and the failure process of interest. This allows us to address both independence assumptions and the transportability of study results. It is shown that transportability cannot be guaranteed in the absence of auxiliary information on the population. Conditions that produce dependent selection and types of auxiliary data are discussed and illustrated in numerical studies. The proposed framework is applied to a study of the risk of psoriatic arthritis in persons with psoriasis.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513408","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