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Unveiling Schizophrenia: a study with generalized functional linear mixed model via the investigation of functional random effects. 揭示精神分裂症:基于功能随机效应的广义泛函线性混合模型研究。
IF 1.8 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae049
Rongxiang Rui, Wei Xiong, Jianxin Pan, Maozai Tian

Previous studies have identified attenuated pre-speech activity and speech sound suppression in individuals with Schizophrenia, with similar patterns observed in basic tasks entailing button-pressing to perceive a tone. However, it remains unclear whether these patterns are uniform across individuals or vary from person to person. Motivated by electroencephalographic (EEG) data from a Schizophrenia study, we develop a generalized functional linear mixed model (GFLMM) for repeated measurements by incorporating subject-specific functional random effects associated with multiple functional predictors. To assess the significance of these functional effects, we employ two different multivariate functional principal component analysis methods, which transform the GFLMM into a conventional generalized linear mixed model, thereby facilitating its implementation with standard software. Furthermore, we introduce a cutting-edge testing approach utilizing working responses to detect both subject-specific and predictor-specific functional random effects. Monte Carlo simulation studies demonstrate the effectiveness of our proposed testing method. Application of the proposed methods to the Schizophrenia data reveals significant subject-specific effects of human brain activity in the frontal zone (Fz) and the central zone (Cz), providing valuable insights into the potential variations among individuals, from healthy controls to those diagnosed with Schizophrenia.

先前的研究已经发现,精神分裂症患者的言语前活动和语音抑制减弱,在需要按下按钮来感知音调的基本任务中也观察到类似的模式。然而,目前尚不清楚这些模式是否在个体之间是一致的,还是因人而异。受一项精神分裂症研究的脑电图(EEG)数据的启发,我们开发了一种广义功能线性混合模型(GFLMM),通过纳入与多个功能预测因子相关的受试者特异性功能随机效应,用于重复测量。为了评估这些功能效应的重要性,我们采用了两种不同的多元功能主成分分析方法,将GFLMM转换为传统的广义线性混合模型,从而便于在标准软件中实现。此外,我们引入了一种尖端的测试方法,利用工作反应来检测受试者特定和预测者特定的功能随机效应。蒙特卡罗仿真研究证明了我们所提出的测试方法的有效性。将所提出的方法应用于精神分裂症数据,揭示了人类大脑额叶区(Fz)和中央区(Cz)活动的显著主体特异性影响,为从健康对照到被诊断为精神分裂症的个体之间的潜在差异提供了有价值的见解。
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引用次数: 0
A marginal structural model for normal tissue complication probability. 正常组织并发症概率的边际结构模型。
IF 1.8 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae019
Thai-Son Tang, Zhihui Liu, Ali Hosni, John Kim, Olli Saarela

The goal of radiation therapy for cancer is to deliver prescribed radiation dose to the tumor while minimizing dose to the surrounding healthy tissues. To evaluate treatment plans, the dose distribution to healthy organs is commonly summarized as dose-volume histograms (DVHs). Normal tissue complication probability (NTCP) modeling has centered around making patient-level risk predictions with features extracted from the DVHs, but few have considered adapting a causal framework to evaluate the safety of alternative treatment plans. We propose causal estimands for NTCP based on deterministic and stochastic interventions, as well as propose estimators based on marginal structural models that impose bivariable monotonicity between dose, volume, and toxicity risk. The properties of these estimators are studied through simulations, and their use is illustrated in the context of radiotherapy treatment of anal canal cancer patients.

癌症放射治疗的目标是将规定的放射剂量输送到肿瘤,同时尽量减少对周围健康组织的剂量。为了评估治疗计划,通常将健康器官的剂量分布总结为剂量-体积直方图(DVH)。正常组织并发症概率(NTCP)建模的核心是利用从剂量-体积直方图中提取的特征进行患者层面的风险预测,但很少有人考虑采用因果框架来评估替代治疗方案的安全性。我们提出了基于确定性和随机性干预的 NTCP 因果估计值,并提出了基于边际结构模型的估计值,这些模型在剂量、容量和毒性风险之间施加了双变量单调性。通过模拟研究了这些估计器的特性,并以肛管癌患者的放疗治疗为例说明了它们的应用。
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引用次数: 0
Incorporating prior information in gene expression network-based cancer heterogeneity analysis. 在基于基因表达网络的癌症异质性分析中纳入先验信息。
IF 1.8 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae028
Rong Li, Shaodong Xu, Yang Li, Zuojian Tang, Di Feng, James Cai, Shuangge Ma

Cancer is molecularly heterogeneous, with seemingly similar patients having different molecular landscapes and accordingly different clinical behaviors. In recent studies, gene expression networks have been shown as more effective/informative for cancer heterogeneity analysis than some simpler measures. Gene interconnections can be classified as "direct" and "indirect," where the latter can be caused by shared genomic regulators (such as transcription factors, microRNAs, and other regulatory molecules) and other mechanisms. It has been suggested that incorporating the regulators of gene expressions in network analysis and focusing on the direct interconnections can lead to a deeper understanding of the more essential gene interconnections. Such analysis can be seriously challenged by the large number of parameters (jointly caused by network analysis, incorporation of regulators, and heterogeneity) and often weak signals. To effectively tackle this problem, we propose incorporating prior information contained in the published literature. A key challenge is that such prior information can be partial or even wrong. We develop a two-step procedure that can flexibly accommodate different levels of prior information quality. Simulation demonstrates the effectiveness of the proposed approach and its superiority over relevant competitors. In the analysis of a breast cancer dataset, findings different from the alternatives are made, and the identified sample subgroups have important clinical differences.

癌症具有分子异质性,看似相似的患者具有不同的分子图谱,因此临床表现也不尽相同。最近的研究表明,基因表达网络比一些简单的测量方法更能有效地分析癌症的异质性。基因之间的相互联系可分为 "直接 "和 "间接 "两种,后者可能是由共享的基因组调控因子(如转录因子、microRNA 和其他调控分子)和其他机制造成的。有人认为,将基因表达的调控因子纳入网络分析并关注直接的相互联系,可以加深对更本质的基因相互联系的理解。这种分析可能会受到大量参数(由网络分析、纳入调控因子和异质性共同造成)和信号通常较弱的严重挑战。为有效解决这一问题,我们建议将已发表文献中包含的先验信息纳入其中。一个关键的挑战是,这些先验信息可能是片面的,甚至是错误的。我们开发了一种两步程序,可以灵活地适应不同程度的先验信息质量。仿真证明了所提方法的有效性及其优于相关竞争者的优势。在对乳腺癌数据集的分析中,我们得出了与其他方法不同的结论,而且所确定的样本亚群具有重要的临床差异。
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引用次数: 0
Shared parameter modeling of longitudinal data allowing for possibly informative visiting process and terminal event. 纵向数据的共享参数建模,允许可能有信息的访问过程和终端事件。
IF 1.8 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae041
Christos Thomadakis, Loukia Meligkotsidou, Nikos Pantazis, Giota Touloumi

Joint modeling of longitudinal and time-to-event data, particularly through shared parameter models (SPMs), is a common approach for handling longitudinal marker data with an informative terminal event. A critical but often neglected assumption in this context is that the visiting/observation process is noninformative, depending solely on past marker values and visit times. When this assumption fails, the visiting process becomes informative, resulting potentially to biased SPM estimates. Existing methods generally rely on a conditional independence assumption, positing that the marker model, visiting process, and time-to-event model are independent given shared or correlated random effects. Moreover, they are typically built on an intensity-based visiting process using calendar time. This study introduces a unified approach for jointly modeling a normally distributed marker, the visiting process, and time-to-event data in the form of competing risks. Our model conditions on the history of observed marker values, prior visit times, the marker's random effects, and possibly a frailty term independent of the random effects. While our approach aligns with the shared-parameter framework, it does not presume conditional independence between the processes. Additionally, the visiting process can be defined on either a gap time scale, via proportional hazard models, or a calendar time scale, via proportional intensity models. Through extensive simulation studies, we assess the performance of our proposed methodology. We demonstrate that disregarding an informative visiting process can yield significantly biased marker estimates. However, misspecification of the visiting process can also lead to biased estimates. The gap time formulation exhibits greater robustness compared to the intensity-based model when the visiting process is misspecified. In general, enriching the visiting process with prior visit history enhances performance. We further apply our methodology to real longitudinal data from HIV, where visit frequency varies substantially among individuals.

纵向数据和时间到事件数据的联合建模,特别是通过共享参数模型(SPM),是处理具有信息性终端事件的纵向标记数据的常用方法。在这种情况下,一个关键但经常被忽视的假设是,访问/观测过程是非信息性的,完全依赖于过去的标记值和访问时间。当这一假设失效时,访问过程就变成了信息过程,从而可能导致 SPM 估计值出现偏差。现有方法一般依赖于条件独立性假设,即在共享或相关随机效应下,标记模型、访问过程和时间到事件模型是独立的。此外,这些方法通常建立在使用日历时间的基于强度的访问过程之上。本研究引入了一种统一的方法,以竞争风险的形式对正态分布的标记、访问过程和时间到事件数据进行联合建模。我们的模型以观察到的标记值历史、之前的访问时间、标记的随机效应以及可能独立于随机效应的虚弱项为条件。虽然我们的方法与共享参数框架一致,但并不假定过程之间的条件独立性。此外,探视过程既可以通过比例危险模型在间隙时间尺度上定义,也可以通过比例强度模型在日历时间尺度上定义。通过大量的模拟研究,我们评估了我们提出的方法的性能。我们证明,忽略信息丰富的访问过程会导致标记估计值严重偏差。然而,对访问过程的错误描述也会导致有偏差的估计。与基于强度的模型相比,间隙时间模型在访问过程被错误定义时表现出更强的稳健性。一般来说,用先前的访问历史来丰富访问过程可以提高性能。我们进一步将我们的方法应用于艾滋病的真实纵向数据,在这些数据中,不同个体的访问频率存在很大差异。
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引用次数: 0
Bipartite interference and air pollution transport: estimating health effects of power plant interventions.
IF 1.8 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae051
Corwin Zigler, Vera Liu, Fabrizia Mealli, Laura Forastiere

Evaluating air quality interventions is confronted with the challenge of interference since interventions at a particular pollution source likely impact air quality and health at distant locations, and air quality and health at any given location are likely impacted by interventions at many sources. The structure of interference in this context is dictated by complex atmospheric processes governing how pollution emitted from a particular source is transformed and transported across space and can be cast with a bipartite structure reflecting the two distinct types of units: (i) interventional units on which treatments are applied or withheld to change pollution emissions; and (ii) outcome units on which outcomes of primary interest are measured. We propose new estimands for bipartite causal inference with interference that construe two components of treatment: a "key-associated" (or "individual") treatment and an "upwind" (or "neighborhood") treatment. Estimation is carried out using a covariate adjustment approach based on a joint propensity score. A reduced-complexity atmospheric model characterizes the structure of the interference network by modeling the movement of air parcels through time and space. The new methods are deployed to evaluate the effectiveness of installing flue-gas desulfurization scrubbers on 472 coal-burning power plants (the interventional units) in reducing Medicare hospitalizations among 21,577,552 Medicare beneficiaries residing across 25,553 ZIP codes in the United States (the outcome units).

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引用次数: 0
Recoverability of causal effects under presence of missing data: a longitudinal case study. 数据缺失情况下因果效应的可恢复性:纵向案例研究。
IF 1.8 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae044
Anastasiia Holovchak, Helen McIlleron, Paolo Denti, Michael Schomaker

Missing data in multiple variables is a common issue. We investigate the applicability of the framework of graphical models for handling missing data to a complex longitudinal pharmacological study of children with HIV treated with an efavirenz-based regimen as part of the CHAPAS-3 trial. Specifically, we examine whether the causal effects of interest, defined through static interventions on multiple continuous variables, can be recovered (estimated consistently) from the available data only. So far, no general algorithms are available to decide on recoverability, and decisions have to be made on a case-by-case basis. We emphasize the sensitivity of recoverability to even the smallest changes in the graph structure, and present recoverability results for three plausible missingness-directed acyclic graphs (m-DAGs) in the CHAPAS-3 study, informed by clinical knowledge. Furthermore, we propose the concept of a "closed missingness mechanism": if missing data are generated based on this mechanism, an available case analysis is admissible for consistent estimation of any statistical or causal estimand, even if data are missing not at random. Both simulations and theoretical considerations demonstrate how, in the assumed MNAR setting of our study, a complete or available case analysis can be superior to multiple imputation, and estimation results vary depending on the assumed missingness DAG. Our analyses demonstrate an innovative application of missingness DAGs to complex longitudinal real-world data, while highlighting the sensitivity of the results with respect to the assumed causal model.

多个变量的缺失数据是一个常见问题。我们研究了处理缺失数据的图形模型框架在一项复杂的纵向药理学研究中的适用性,该研究是 CHAPAS-3 试验的一部分,研究对象是接受以依非韦伦为基础的方案治疗的 HIV 感染儿童。具体来说,我们研究了通过对多个连续变量的静态干预所确定的相关因果效应是否可以仅从现有数据中恢复(一致估计)。到目前为止,还没有可用来决定可恢复性的通用算法,必须根据具体情况做出决定。我们强调了可恢复性对图结构中最小变化的敏感性,并介绍了 CHAPAS-3 研究中三个可信的缺失指向无环图(m-DAG)的可恢复性结果,这些结果是以临床知识为基础的。此外,我们还提出了 "封闭缺失机制 "的概念:如果缺失数据是基于这种机制产生的,那么即使数据不是随机缺失,也可以通过可用的病例分析对任何统计或因果估计进行一致的估计。模拟和理论考虑都表明,在我们研究的假定 MNAR 设置中,完整或可用案例分析如何优于多重估算,估算结果因假定的缺失 DAG 而异。我们的分析展示了缺失 DAG 在复杂的纵向真实世界数据中的创新应用,同时强调了结果对假定因果模型的敏感性。
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引用次数: 0
Functional quantile principal component analysis. 功能量化主成分分析
IF 1.8 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae040
Álvaro Méndez-Civieta, Ying Wei, Keith M Diaz, Jeff Goldsmith

This paper introduces functional quantile principal component analysis (FQPCA), a dimensionality reduction technique that extends the concept of functional principal components analysis (FPCA) to the examination of participant-specific quantiles curves. Our approach borrows strength across participants to estimate patterns in quantiles, and uses participant-level data to estimate loadings on those patterns. As a result, FQPCA is able to capture shifts in the scale and distribution of data that affect participant-level quantile curves, and is also a robust methodology suitable for dealing with outliers, heteroscedastic data or skewed data. The need for such methodology is exemplified by physical activity data collected using wearable devices. Participants often differ in the timing and intensity of physical activity behaviors, and capturing information beyond the participant-level expected value curves produced by FPCA is necessary for a robust quantification of diurnal patterns of activity. We illustrate our methods using accelerometer data from the National Health and Nutrition Examination Survey, and produce participant-level 10%, 50%, and 90% quantile curves over 24 h of activity. The proposed methodology is supported by simulation results, and is available as an R package.

本文介绍了功能量化主成分分析(FQPCA),这是一种降维技术,它将功能主成分分析(FPCA)的概念扩展到了对特定参与者量化曲线的研究。我们的方法借用不同参与者的力量来估计量化曲线的模式,并使用参与者层面的数据来估计这些模式的载荷。因此,FQPCA 能够捕捉到数据规模和分布中影响参与者水平量化曲线的变化,也是一种适用于处理异常值、异方差数据或倾斜数据的稳健方法。使用可穿戴设备收集的身体活动数据就说明了对这种方法的需求。参与者的体力活动行为在时间和强度上往往各不相同,要想对昼夜活动模式进行稳健的量化,就必须捕捉 FPCA 生成的参与者级预期值曲线以外的信息。我们使用美国国家健康与营养调查的加速度计数据来说明我们的方法,并生成了参与者水平的 10%、50% 和 90% 的 24 小时活动量定量曲线。我们提出的方法得到了模拟结果的支持,并以 R 软件包的形式提供。
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引用次数: 0
Understanding the opioid syndemic in North Carolina: A novel approach to modeling and identifying factors.
IF 1.8 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae052
Eva Murphy, David Kline, Kathleen L Egan, Kathryn E Lancaster, William C Miller, Lance A Waller, Staci A Hepler

The opioid epidemic is a significant public health challenge in North Carolina, but limited data restrict our understanding of its complexity. Examining trends and relationships among different outcomes believed to reflect opioid misuse provides an alternative perspective to understand the opioid epidemic. We use a Bayesian dynamic spatial factor model to capture the interrelated dynamics within six different county-level outcomes, such as illicit opioid overdose deaths, emergency department visits related to drug overdose, treatment counts for opioid use disorder, patients receiving prescriptions for buprenorphine, and newly diagnosed cases of acute and chronic hepatitis C virus and human immunodeficiency virus. We design the factor model to yield meaningful interactions among predefined subsets of these outcomes, causing a departure from the conventional lower triangular structure in the loadings matrix and leading to familiar identifiability issues. To address this challenge, we propose a novel approach that involves decomposing the loadings matrix within a Markov chain Monte Carlo algorithm, allowing us to estimate the loadings and factors uniquely. As a result, we gain a better understanding of the spatio-temporal dynamics of the opioid epidemic in North Carolina.

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引用次数: 0
Regression and alignment for functional data and network topology. 功能数据和网络拓扑的回归和配准。
IF 1.8 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae026
Danni Tu, Julia Wrobel, Theodore D Satterthwaite, Jeff Goldsmith, Ruben C Gur, Raquel E Gur, Jan Gertheiss, Dani S Bassett, Russell T Shinohara

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of preprocessing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

在大脑中,功能连接形成了一个网络,其拓扑组织可以通过图论网络诊断来描述。其中包括群落结构的特征,如模块化和参与系数,这些特征已被证明会随着儿童和青少年时期的变化而变化。为了研究功能网络的这种变化是否与发育过程中认知能力的变化有关,网络研究通常依赖于对预处理参数的任意选择,特别是网络边缘的比例阈值。由于参数的选择会影响网络诊断的值,从而影响下游结论,因此我们建议将网络诊断概念化为参数的函数,以规避这种选择。与单一数值不同,网络诊断曲线描述了多个尺度的连接组拓扑结构--从最稀疏的最强边缘组到整个边缘集。为了将这些曲线与执行功能和其他协变量联系起来,我们使用了标量-功能回归,这比以往网络神经科学中使用的基于功能数据的模型更加灵活。然后,我们考虑了网络之间的系统性差异如何表现为诊断曲线的不对齐,并因此提出了一种包含其他变量辅助信息的监督曲线对齐方法。我们的算法通过迭代、惩罚和非线性似然优化来执行函数回归和配准。这种方法有望提高神经科学研究的可解释性和可推广性,因为神经科学研究的目标是研究函数值和标量值混合测量的异质性。
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引用次数: 0
Scalable randomized kernel methods for multiview data integration and prediction with application to Coronavirus disease.
IF 1.8 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxaf001
Sandra E Safo, Han Lu

There is still more to learn about the pathobiology of coronavirus disease (COVID-19) despite 4 years of the pandemic. A multiomics approach offers a comprehensive view of the disease and has the potential to yield deeper insight into the pathogenesis of the disease. Previous multiomics integrative analysis and prediction studies for COVID-19 severity and status have assumed simple relationships (ie linear relationships) between omics data and between omics and COVID-19 outcomes. However, these linear methods do not account for the inherent underlying nonlinear structure associated with these different types of data. The motivation behind this work is to model nonlinear relationships in multiomics and COVID-19 outcomes, and to determine key multidimensional molecules associated with the disease. Toward this goal, we develop scalable randomized kernel methods for jointly associating data from multiple sources or views and simultaneously predicting an outcome or classifying a unit into one of 2 or more classes. We also determine variables or groups of variables that best contribute to the relationships among the views. We use the idea that random Fourier bases can approximate shift-invariant kernel functions to construct nonlinear mappings of each view and we use these mappings and the outcome variable to learn view-independent low-dimensional representations. We demonstrate the effectiveness of the proposed methods through extensive simulations. When the proposed methods were applied to gene expression, metabolomics, proteomics, and lipidomics data pertaining to COVID-19, we identified several molecular signatures for COVID-19 status and severity. Our results agree with previous findings and suggest potential avenues for future research. Our algorithms are implemented in Pytorch and interfaced in R and available at: https://github.com/lasandrall/RandMVLearn.

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引用次数: 0
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