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Bayesian Pliable Lasso With Horseshoe Prior for Interaction Effects in GLMs With Missing Responses. 马蹄形先验贝叶斯柔性套索对缺失响应GLMs相互作用效应的研究。
IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2026-02-01 DOI: 10.1002/sim.70406
The Tien Mai

Sparse regression problems, where the goal is to identify a small set of relevant predictors, often require modeling not only main effects but also meaningful interactions through other variables. While the pliable lasso has emerged as a powerful frequentist tool for modeling such interactions under strong heredity constraints, it lacks a natural framework for uncertainty quantification and incorporation of prior knowledge. In this paper, we propose a Bayesian pliable lasso that extends this approach by placing sparsity-inducing priors, such as the horseshoe, on both main and interaction effects. The hierarchical prior structure enforces heredity constraints while adaptively shrinking irrelevant coefficients and allowing important effects to persist. We extend this framework to generalized linear models and develop a tailored approach to handle missing responses. To facilitate posterior inference, we develop an efficient Gibbs sampling algorithm based on a reparameterization of the horseshoe prior. Our Bayesian framework yields sparse, interpretable interaction structures, and principled measures of uncertainty. Through simulations and real-data studies, we demonstrate its advantages over existing methods in recovering complex interaction patterns under both complete and incomplete data. Our method is implemented in the package hspliable available on Github: https://github.com/tienmt/hspliable.

稀疏回归问题的目标是识别一小组相关预测因子,通常不仅需要对主要影响进行建模,还需要对其他变量之间有意义的相互作用进行建模。虽然柔性套索已经成为一种强大的频率学工具,可以在强遗传约束下对这种相互作用进行建模,但它缺乏不确定性量化和整合先验知识的自然框架。在本文中,我们提出了一个贝叶斯柔性套索,通过在主效应和交互效应上放置稀疏诱导先验(如马蹄铁)来扩展该方法。在自适应地缩小不相关系数并允许重要影响持续存在的同时,分层先验结构加强了遗传约束。我们将此框架扩展到广义线性模型,并开发了一种定制的方法来处理缺失响应。为了便于后验推理,我们开发了一种基于马蹄先验重新参数化的高效吉布斯采样算法。我们的贝叶斯框架产生稀疏的、可解释的交互结构,以及不确定性的原则度量。通过仿真和实际数据研究,证明了该方法在完全和不完全数据下恢复复杂交互模式方面优于现有方法。我们的方法在Github上的hsplable包中实现:https://github.com/tienmt/hspliable。
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引用次数: 0
An Improved Misclassification Simulation Extrapolation (MC-SIMEX) Algorithm. 一种改进的误分类模拟外推(MC-SIMEX)算法。
IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2026-02-01 DOI: 10.1002/sim.70418
Varadan Sevilimedu, Lili Yu

Misclassification Simulation-Extrapolation (MC-SIMEX) is an established method to correct for misclassification in binary covariates in a model. It involves the use of a simulation component which simulates pseudo-datasets with added degree of misclassification in the binary covariate and an extrapolation component which models the covariate's regression coefficients obtained at each level of misclassification using a quadratic function. This quadratic function is then used to extrapolate the covariate's regression coefficients to a point of "no error" in the classification of the binary covariate under question. However, extrapolation functions are not usually known accurately beforehand and are therefore only approximated versions. In this article, we propose an innovative method that uses the exact (not approximated) extrapolation function through the use of a derived relationship between the naïve regression coefficient estimates and the true coefficients in generalized linear models. Simulation studies are conducted to study and compare the numerical properties of the resulting estimator to the original MC-SIMEX estimator. Real data analysis using colon cancer data from the MSKCC cancer registry is also provided.

错误分类模拟外推法(MC-SIMEX)是一种修正模型中二元协变量错误分类的方法。它涉及使用模拟组件来模拟二元协变量中添加了错误分类程度的伪数据集,以及使用二次函数对每个错误分类级别上获得的协变量回归系数进行建模的外推组件。然后使用这个二次函数将协变量的回归系数外推到所讨论的二元协变量分类中的“无误差”点。然而,外推函数通常事先不知道准确,因此只是近似的版本。在本文中,我们提出了一种创新的方法,通过使用广义线性模型中naïve回归系数估计值与真实系数之间的推导关系,使用精确(非近似)外推函数。进行了仿真研究,研究并比较了所得估计器与原始MC-SIMEX估计器的数值特性。还提供了使用来自MSKCC癌症登记处的结肠癌数据的真实数据分析。
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引用次数: 0
Improved Centile Estimation by Transformation And/Or Adaptive Smoothing of the Explanatory Variable. 基于解释变量变换和/或自适应平滑的改进百分位估计。
IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2026-02-01 DOI: 10.1002/sim.70414
R A Rigby, D M Stasinopoulos, T J Cole

A popular approach to growth reference centile estimation is the LMS (Lambda-Mu-Sigma) method, which assumes a parametric distribution for response variable Y $$ Y $$ and fits the location, scale and shape parameters of the distribution of Y $$ Y $$ as smooth functions of explanatory variable X $$ X $$ . This article provides two methods, transformation and adaptive smoothing, for improving the centile estimation when there is high curvature (i.e., rapid change in slope) with respect to X $$ X $$ in one or more of the Y $$ Y $$ distribution parameters. In general, high curvature is reduced (i.e., attenuated or dampened) by smoothing. In the first method, X $$ X $$ is transformed to variable T $$ T $$ to reduce this high curvature, and the Y $$ Y $$ distribution parameters are fitted as smooth functions of T $$ T $$ . Three different transformations of X $$ X $$ are described. In the second method, the Y $$ Y $$ distribution parameters are adaptively smoothed against X $$ X $$ by allowing the smoothing parameter itself to vary continuously with Y $$ Y $$ . Simulations are used to compare the performance of the two methods. Three examples show how the process can lead to substantially smoother and better fitting centiles.

一种常用的生长参考百分位数估计方法是LMS (Lambda-Mu-Sigma)方法,该方法假设响应变量Y $$ Y $$的参数分布,并将Y $$ Y $$分布的位置、规模和形状参数拟合为解释变量X $$ X $$的光滑函数。本文提供了变换和自适应平滑两种方法,用于在一个或多个Y $$ Y $$分布参数中存在相对于X $$ X $$的高曲率(即斜率的快速变化)时改进分位数估计。一般来说,通过平滑可以减少高曲率(即衰减或阻尼)。在第一种方法中,将X $$ X $$转换为变量T $$ T $$以减小这种高曲率,并将Y $$ Y $$分布参数拟合为T $$ T $$的光滑函数。描述了X $$ X $$的三种不同变换。在第二种方法中,通过允许平滑参数本身随Y $$ Y $$连续变化,Y $$ Y $$分布参数针对X $$ X $$进行自适应平滑。通过仿真比较了两种方法的性能。三个例子显示了该过程如何导致更平滑和更好的拟合百分位数。
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引用次数: 0
Robust Distribution-Free Tests for the Linear Model. 线性模型的鲁棒无分布检验。
IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2026-02-01 DOI: 10.1002/sim.70404
Torey Hilbert, Steven N MacEachern, Yuan Zhang

Recently, there has been growing concern about heavy-tailed and skewed noise in biological data. We introduce RobustPALMRT, a flexible permutation framework for testing the association of a covariate of interest adjusted for control covariates. RobustPALMRT controls type I error rate for finite-samples, even in the presence of heavy-tailed or skewed noise. The new framework expands the scope of state-of-the-art tests in three directions. First, our method applies to robust and quantile regressions, even with the necessary hyper-parameter tuning. Second, by separating model-fitting and model-evaluation, we discover that performance improves when using a robust loss function in the model-evaluation step, regardless of how the model is fit. Third, we allow fitting multiple models to detect specialized features of interest in a distribution. To demonstrate this, we introduce DispersionPALMRT, which tests for differences in dispersion between treatment and control groups. We establish theoretical guarantees, identify settings where our method has greater power than existing methods, and analyze existing immunological data on Long-COVID patients. Using RobustPALMRT, we unveil novel differences between Long-COVID patients and others even in the presence of highly skewed noise.

最近,人们越来越关注生物数据中的重尾和偏斜噪声。我们引入了RobustPALMRT,这是一个灵活的排列框架,用于测试对控制协变量进行调整的协变量的关联。RobustPALMRT控制有限样本的I型错误率,即使在存在重尾或偏斜噪声的情况下。新框架从三个方面扩大了最先进测试的范围。首先,我们的方法适用于鲁棒和分位数回归,即使有必要的超参数调整。其次,通过分离模型拟合和模型评估,我们发现当在模型评估步骤中使用鲁棒损失函数时,无论模型如何拟合,性能都有所提高。第三,我们允许拟合多个模型来检测分布中感兴趣的专门特征。为了证明这一点,我们引入了DispersionPALMRT,它测试了治疗组和对照组之间的分散差异。我们建立了理论保证,确定了我们的方法比现有方法更有效的设置,并分析了长期covid患者的现有免疫学数据。使用RobustPALMRT,即使在存在高度偏斜噪声的情况下,我们也揭示了长covid患者与其他人之间的新差异。
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引用次数: 0
Is UWLS Really Better for Medical Research? UWLS真的更适合医学研究吗?
IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2026-02-01 DOI: 10.1002/sim.70411
Sanghyun Hong, W Robert Reed

This study evaluates the performance of the Unrestricted Weighted Least Squares (UWLS) estimator in meta-analyses of medical research. Using a large-scale simulation approach, it addresses the limitations of model selection criteria in small-sample contexts. Prior research using the Cochrane Database of Systematic Reviews (CDSR) reported that UWLS outperformed Random Effects (RE) and, in some cases, Fixed Effect (FE) estimators when assessed using AIC and BIC. However, we show that idiosyncratic characteristics of the CDSR datasets, notably their small sample sizes and weak-signal settings (where key parameters are often small in magnitude), undermine the reliability of AIC and BIC for model selection. Accordingly, we simulate 108 000 datasets mirroring the original CDSR data. This allows us to know the true model parameters and evaluate the estimators more accurately. While all estimators performed similarly with respect to bias and efficiency, RE consistently produced more accurate standard errors than UWLS, making confidence intervals and hypothesis testing more reliable. The comparison with FE was less clear. We therefore recommend continued use of the RE estimator as a reliable general-purpose approach for medical research, with the choice between UWLS and FE made in light of the likely extent of effect heterogeneity in the data.

本研究评估非限制加权最小二乘(UWLS)估计量在医学研究荟萃分析中的表现。使用大规模模拟方法,它解决了小样本环境中模型选择标准的局限性。先前使用Cochrane系统评价数据库(CDSR)的研究报告称,当使用AIC和BIC评估时,UWLS优于随机效应(RE),在某些情况下优于固定效应(FE)估计器。然而,我们表明,CDSR数据集的特质特征,特别是它们的小样本量和弱信号设置(其中关键参数通常很小),破坏了AIC和BIC模型选择的可靠性。因此,我们模拟了108,000个镜像原始CDSR数据集。这使我们能够知道真实的模型参数并更准确地评估估计器。虽然所有估计器在偏倚和效率方面的表现相似,但RE始终比UWLS产生更准确的标准误差,使置信区间和假设检验更可靠。与FE的比较不太清楚。因此,我们建议继续使用RE估计器作为医学研究中可靠的通用方法,并根据数据中效应异质性的可能程度在UWLS和FE之间进行选择。
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引用次数: 0
Patient-Centric Pragmatic Clinical Trials: Opening the DOOR. 以患者为中心的实用临床试验:打开大门。
IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2026-02-01 DOI: 10.1002/sim.70328
Scott R Evans, Qihang Wu, Toshimitsu Hamasaki

Randomized clinical trials are the gold standard for evaluating the benefits and harms of interventions, though they often fail to provide the necessary evidence to inform medical decision-making. Primary reasons are failure to recognize the most important questions for informing clinical practice, and that traditional approaches do not directly address these most important questions, and subsequently not using these most important questions as the motivation for the design, monitoring, analysis, and reporting of clinical trials. The standard approach of analyzing one outcome at a time fails to incorporate associations between or the cumulative nature of multiple outcomes in individual patients, suffers from competing risk complexities during interpretation of individual outcomes, fails to recognize important gradations of patient-centric responses, and since efficacy and safety analyses are often conducted on different populations, benefit:risk estimands and generalizability are unclear. Cardiovascular event prevention trials typically utilize: (1) major adverse cardiovascular events (MACE), for example, stroke, myocardial infarction, and death as the primary endpoint, which fails to recognize multiple events or the differential importance of events, and (2) relative risk models which rely on robustness-challenging modeling assumptions and are contraindicated in benefit:risk and multiple outcome evaluation. The Desirability Of Outcome Ranking (DOOR) is a paradigm for the design, data monitoring, analysis, interpretation, and reporting of clinical trials based on comprehensive patient-centric benefit:risk evaluation, developed to address these issues and advance clinical trial science. The rationale and the methodology for the design and analyses for the DOOR paradigm are described. The methods are illustrated using an example. Freely available online tools for the design and analysis of studies implementing the DOOR are provided.

随机临床试验是评估干预措施利弊的黄金标准,尽管它们往往不能提供必要的证据来指导医疗决策。主要原因是未能认识到为临床实践提供信息的最重要问题,传统方法不能直接解决这些最重要的问题,随后也没有将这些最重要的问题作为临床试验设计、监测、分析和报告的动机。一次分析一个结果的标准方法未能纳入个体患者中多个结果之间的关联或累积性质,在解释个体结果时存在相互竞争的风险复杂性,未能识别以患者为中心的反应的重要分级,并且由于疗效和安全性分析通常在不同人群中进行,因此获益:风险估计和推广尚不清楚。心血管事件预防试验通常使用:(1)主要不良心血管事件(MACE),例如中风、心肌梗死和死亡作为主要终点,它不能识别多个事件或事件的不同重要性;(2)相对风险模型依赖于具有鲁棒性的建模假设,并且在获益、风险和多结果评估中是禁忌的。结果期望排序(DOOR)是一种基于以患者为中心的综合获益风险评估的临床试验设计、数据监测、分析、解释和报告的范式,旨在解决这些问题并推进临床试验科学。描述了DOOR范式的设计和分析的基本原理和方法。通过一个实例说明了这些方法。提供了免费的在线工具,用于设计和分析实施DOOR的研究。
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引用次数: 0
A Functional Joint Model for Survival and Multivariate Sparse Functional Data in Multi-Cohort Alzheimer's Disease Study. 多队列阿尔茨海默病研究中生存和多变量稀疏功能数据的功能联合模型。
IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2026-02-01 DOI: 10.1002/sim.70442
Wenyi Wang, Luo Xiao, Ruonan Li, Sheng Luo

We develop an integrative joint model for multivariate sparse functional and survival data to analyze Alzheimer's disease (AD) across multiple studies. To address missing-by-design outcomes in multi-cohort studies, our approach extends the multivariate functional mixed model (MFMM), which integrates longitudinal outcomes to extract shared disease progression trajectories and links these outcomes to time-to-event data through a parsimonious survival model. This framework balances flexibility and interpretability by modeling shared progression trajectories while accommodating cohort-specific mean functions and survival parameters. For efficient estimation, we incorporate penalized splines into an EM algorithm. Application to three AD cohorts demonstrates the model's ability to capture disease trajectories and account for inter-cohort variability. Simulation studies confirm its robustness and accuracy, highlighting its value in advancing the understanding of AD progression and supporting clinical decision-making in multi-cohort settings.

我们开发了一个多变量稀疏功能和生存数据的综合联合模型,用于跨多个研究分析阿尔茨海默病(AD)。为了解决多队列研究中设计缺失的结果,我们的方法扩展了多变量功能混合模型(MFMM),该模型集成了纵向结果以提取共享的疾病进展轨迹,并通过简约的生存模型将这些结果与事件时间数据联系起来。该框架通过建模共享的进展轨迹来平衡灵活性和可解释性,同时适应特定队列的平均函数和生存参数。为了有效估计,我们将惩罚样条合并到EM算法中。对三个AD队列的应用证明了该模型捕捉疾病轨迹和解释队列间变异性的能力。模拟研究证实了它的稳健性和准确性,强调了它在促进对阿尔茨海默病进展的理解和支持多队列环境下的临床决策方面的价值。
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引用次数: 0
Group Lasso Based Selection for High-Dimensional Mediation Analysis. 基于群体套索的高维中介分析选择。
IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2026-02-01 DOI: 10.1002/sim.70351
Allan Jérolon, Flora Alarcon, Florence Pittion, Magali Richard, Olivier François, Etienne Birmelé, Vittorio Perduca

Mediation analysis aims to identify and estimate the effect of an exposure on an outcome that is mediated through one or more intermediate variables. In the presence of multiple intermediate variables, two pertinent methodological questions arise: estimating mediated effects when mediators are correlated, and performing high-dimensional mediation analyses when the number of mediators exceeds the sample size. This paper presents a two-step procedure for high-dimensional mediation analyses. The first step selects a reduced number of candidate mediators using an ad-hoc lasso penalty. The second step applies a procedure we previously developed to estimate the mediated effects, accounting for the correlation structure among the retained candidate mediators. We compare the performance of the proposed two-step procedure with state-of-the-art methods using simulated data. Additionally, we demonstrate its practical application by estimating the causal role of DNA methylation (DNAm) in the pathway between smoking and rheumatoid arthritis (RA) using real data.

中介分析旨在识别和估计暴露对通过一个或多个中间变量中介的结果的影响。在存在多个中间变量的情况下,出现了两个相关的方法学问题:当中介因子相关时估计中介效应,当中介因子数量超过样本量时进行高维中介分析。本文提出了一个高维中介分析的两步程序。第一步使用特别套索惩罚选择减少数量的候选中介。第二步应用我们之前开发的程序来估计中介效应,考虑保留的候选中介之间的相关结构。我们比较了性能提出的两步程序与国家的最先进的方法使用模拟数据。此外,我们通过使用真实数据估计DNA甲基化(DNAm)在吸烟和类风湿性关节炎(RA)之间的途径中的因果作用来证明其实际应用。
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引用次数: 0
Assessing the Benefits and Burdens of Preventive Interventions. 评估预防干预措施的利益和负担。
IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2026-02-01 DOI: 10.1002/sim.70410
Yi Xiong, Kwun C G Chan, Malka Gorfine, Li Hsu

Cancer prevention is recognized as a key strategy for reducing disease incidence, mortality, and the overall burden on individuals and society. However, determining when to begin preventive interventions presents a significant challenge: starting too early may lead to more interventions and increased lifetime burdens due to repeated administrations, while delaying may miss opportunities to prevent cancer. Evidence-based recommendations require a benefit-burden analysis that weighs life-years gained against the burden of interventions. With the growing availability of large-scale observational data, there is now an opportunity to empirically evaluate these trade-offs. In this paper, we propose a causal framework for assessing the benefit and burden of cancer prevention, using an illness-death model with semi-competing risks. Extensive simulations demonstrate that the proposed estimators are unbiased, with robust inference across realistic scenarios. We apply this approach to a benefit-burden analysis of the preventive screening for colorectal cancer, utilizing data from the large-scale Women's Health Initiative. Our findings suggest that initiating screening at age 50 years achieves the highest life-year gains with an acceptable incremental burden-to-benefit ratio compared to no screening, contributing valuable real-world evidence to the field of preventive cancer interventions.

癌症预防被认为是降低疾病发病率、死亡率以及个人和社会总体负担的关键战略。然而,确定何时开始预防性干预是一项重大挑战:过早开始可能导致更多的干预,并因反复给药而增加终生负担,而拖延可能会错过预防癌症的机会。基于证据的建议需要进行利益负担分析,权衡获得的生命年数与干预措施的负担。随着大规模观测数据的日益可用性,现在有机会对这些权衡进行经验评估。在本文中,我们提出了一个因果框架来评估癌症预防的利益和负担,使用一个半竞争风险的疾病-死亡模型。大量的模拟表明,所提出的估计器是无偏的,具有跨现实场景的鲁棒推断。我们利用大规模妇女健康倡议的数据,将这种方法应用于结直肠癌预防性筛查的利益-负担分析。我们的研究结果表明,与不进行筛查相比,在50岁开始筛查可获得最高的生命年收益,并具有可接受的增量负担-收益比,为预防性癌症干预领域提供了有价值的现实证据。
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引用次数: 0
Multivariate and Online Transfer Learning With Uncertainty Quantification. 不确定量化的多元在线迁移学习。
IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2026-02-01 DOI: 10.1002/sim.70398
Jimmy Hickey, Jonathan P Williams, Brian J Reich, Emily C Hector

Untreated periodontitis causes inflammation within the supporting tissue of the teeth and can ultimately lead to tooth loss. Modeling periodontal outcomes is beneficial as they are difficult and time-consuming to measure, but disparities in representation between demographic groups must be considered. There may not be enough participants to build group-specific models, and it can be ineffective, and even dangerous, to apply a model to participants in an underrepresented group if demographic differences were not considered during training. We propose an extension to the RECaST Bayesian transfer learning framework. Our method jointly models multivariate outcomes, exhibiting significant improvement over the previous univariate RECaST method. Further, we introduce an online approach to model sequential data sets. Negative transfer is mitigated to ensure that the information shared from the other demographic groups does not negatively impact the modeling of the underrepresented participants. The Bayesian framework naturally provides uncertainty quantification on predictions. Especially important in medical applications, our method does not share data between domains. We demonstrate the effectiveness of our method in both predictive performance and uncertainty quantification on simulated data and on a database of dental records from the HealthPartners Institute.

牙周炎未经治疗会导致牙齿的支撑组织发炎,最终导致牙齿脱落。牙周结果建模是有益的,因为测量它们是困难和耗时的,但必须考虑到人口群体之间代表性的差异。可能没有足够的参与者来建立特定群体的模型,如果在培训期间没有考虑人口统计学差异,将模型应用于代表性不足的群体的参与者可能是无效的,甚至是危险的。我们提出了对RECaST贝叶斯迁移学习框架的扩展。我们的方法联合建模多变量结果,比以前的单变量RECaST方法有显著改进。此外,我们还引入了一种在线方法来对序列数据集进行建模。减少负迁移,以确保从其他人口统计群体共享的信息不会对代表性不足的参与者的建模产生负面影响。贝叶斯框架自然地为预测提供了不确定性量化。在医疗应用中尤其重要的是,我们的方法不会在域之间共享数据。我们在模拟数据和HealthPartners研究所牙科记录数据库上证明了我们的方法在预测性能和不确定性量化方面的有效性。
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引用次数: 0
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Statistics in Medicine
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