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Methodological and Statistical Practices of Using Symptom Networks to Evaluate Mental Health Interventions: A Review and Reflections. 使用症状网络评估心理健康干预措施的方法和统计实践:回顾与思考》。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-01 Epub Date: 2024-05-11 DOI: 10.1080/00273171.2024.2335401
Lea Schumacher, Julian Burger, Jette Echterhoff, Levente Kriston

The network approach to psychopathology, which assesses associations between individual symptoms, has recently been applied to evaluate treatments for mental disorders. While various options for conducting network analyses in intervention research exist, an overview and an evaluation of the various approaches are currently missing. Therefore, we conducted a review on network analyses in intervention research. Studies were included if they constructed a symptom network, analyzed data that were collected before, during or after treatment of a mental disorder, and yielded information about the treatment effect. The 56 included studies were reviewed regarding their methodological and analytic strategies. About half of the studies based on data from randomized trials conducted a network intervention analysis, while the other half compared networks between treatment groups. The majority of studies estimated cross-sectional networks, even when repeated measures were available. All but five studies investigated networks on the group level. This review highlights that current methodological practices limit the information that can be gained through network analyses in intervention research. We discuss the strength and limitations of certain methodological and analytic strategies and propose that further work is needed to use the full potential of the network approach in intervention research.

精神病理学的网络分析方法评估个体症状之间的关联,最近已被用于评估精神障碍的治疗方法。虽然在干预研究中进行网络分析有多种选择,但目前还缺少对各种方法的概述和评估。因此,我们对干预研究中的网络分析进行了综述。如果研究构建了症状网络,分析了精神障碍治疗前、治疗中或治疗后收集的数据,并提供了有关治疗效果的信息,则被纳入研究范围。我们对纳入的 56 项研究的方法和分析策略进行了审查。基于随机试验数据的研究中,约有一半进行了网络干预分析,而另一半则对治疗组之间的网络进行了比较。大多数研究对横断面网络进行了估计,即使有重复测量数据也是如此。除五项研究外,其他所有研究都对群体层面的网络进行了调查。本综述强调,目前的方法限制了干预研究中通过网络分析获得的信息。我们讨论了某些方法和分析策略的优势和局限性,并提出需要进一步开展工作,以充分发挥网络方法在干预研究中的潜力。
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引用次数: 0
Bayesian Multivariate Logistic Regression for Superiority and Inferiority Decision-Making under Observable Treatment Heterogeneity. 在可观察到的治疗异质性条件下,用于优劣决策的贝叶斯多变量逻辑回归。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-01 Epub Date: 2024-05-11 DOI: 10.1080/00273171.2024.2337340
Xynthia Kavelaars, Joris Mulder, Maurits Kaptein

The effects of treatments may differ between persons with different characteristics. Addressing such treatment heterogeneity is crucial to investigate whether patients with specific characteristics are likely to benefit from a new treatment. The current paper presents a novel Bayesian method for superiority decision-making in the context of randomized controlled trials with multivariate binary responses and heterogeneous treatment effects. The framework is based on three elements: a) Bayesian multivariate logistic regression analysis with a Pólya-Gamma expansion; b) a transformation procedure to transfer obtained regression coefficients to a more intuitive multivariate probability scale (i.e., success probabilities and the differences between them); and c) a compatible decision procedure for treatment comparison with prespecified decision error rates. Procedures for a priori sample size estimation under a non-informative prior distribution are included. A numerical evaluation demonstrated that decisions based on a priori sample size estimation resulted in anticipated error rates among the trial population as well as subpopulations. Further, average and conditional treatment effect parameters could be estimated unbiasedly when the sample was large enough. Illustration with the International Stroke Trial dataset revealed a trend toward heterogeneous effects among stroke patients: Something that would have remained undetected when analyses were limited to average treatment effects.

不同特征的人接受治疗的效果可能不同。要研究具有特定特征的患者是否可能从新疗法中获益,解决这种治疗异质性问题至关重要。本文提出了一种新颖的贝叶斯方法,用于在具有多变量二元反应和异质性治疗效果的随机对照试验中进行优势决策。该框架基于三个要素:a) 采用 Pólya-Gamma 扩展的贝叶斯多元逻辑回归分析;b) 将获得的回归系数转换为更直观的多元概率量表(即成功概率及其之间的差异)的转换程序;c) 采用预设决策误差率进行治疗比较的兼容决策程序。此外,还包括非信息先验分布下的先验样本量估算程序。数值评估结果表明,基于先验样本量估计的决策可在试验人群和亚人群中产生预期误差率。此外,当样本足够大时,平均和条件治疗效果参数可以无偏估计。国际脑卒中试验数据集的说明显示,脑卒中患者中存在异质性效应的趋势:如果只对平均治疗效果进行分析,则无法发现这种趋势。
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引用次数: 0
Information Matrix Test for Item Response Models Using Stochastic Approximation. 使用随机逼近法进行项目反应模型的信息矩阵测试
IF 3.8 3区 心理学 Q1 Mathematics Pub Date : 2024-05-01 Epub Date: 2024-02-19 DOI: 10.1080/00273171.2024.2310426
Youngjin Han, Yang Liu, Ji Seung Yang
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引用次数: 0
Propensity Score Weighting with Missing Data on Covariates and Clustered Data Structure. 具有协变量缺失数据和聚类数据结构的倾向得分加权。
IF 3.8 3区 心理学 Q1 Mathematics Pub Date : 2024-05-01 Epub Date: 2024-02-20 DOI: 10.1080/00273171.2024.2307529
Xiao Liu

Propensity score (PS) analyses are increasingly popular in behavioral sciences. Two issues often add complexities to PS analyses, including missing data in observed covariates and clustered data structure. In previous research, methods for conducting PS analyses with considering either issue alone were examined. In practice, the two issues often co-occur; but the performance of methods for PS analyses in the presence of both issues has not been evaluated previously. In this study, we consider PS weighting analysis when data are clustered and observed covariates have missing values. A simulation study is conducted to evaluate the performance of different missing data handling methods (complete-case, single-level imputation, or multilevel imputation) combined with different multilevel PS weighting methods (fixed- or random-effects PS models, inverse-propensity-weighting or the clustered weighting, weighted single-level or multilevel outcome models). The results suggest that the bias in average treatment effect estimation can be reduced, by better accounting for clustering in both the missing data handling stage (such as with the multilevel imputation) and the PS analysis stage (such as with the fixed-effects PS model, clustered weighting, and weighted multilevel outcome model). A real-data example is provided for illustration.

倾向得分(PS)分析在行为科学领域越来越受欢迎。有两个问题常常会增加倾向评分分析的复杂性,包括观测协变量数据的缺失和聚类数据结构。在以往的研究中,研究人员研究了在单独考虑其中一个问题的情况下进行 PS 分析的方法。在实践中,这两个问题往往同时存在;但在同时存在这两个问题的情况下,进行 PS 分析的方法的性能以前还没有进行过评估。在本研究中,我们考虑了数据聚类和观测协变量有缺失值时的 PS 加权分析。我们进行了一项模拟研究,以评估不同的缺失数据处理方法(完整病例、单水平估算或多水平估算)与不同的多水平 PS 加权方法(固定或随机效应 PS 模型、反倾向加权或聚类加权、加权单水平或多水平结果模型)相结合的性能。结果表明,通过在缺失数据处理阶段(如多水平估算)和 PS 分析阶段(如固定效应 PS 模型、聚类加权和加权多水平结果模型)更好地考虑聚类,可以减少平均治疗效果估计的偏差。现提供一个真实数据示例以作说明。
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引用次数: 0
Subgrouping with Chain Graphical VAR Models. 使用链式图形 VAR 模型进行分组。
IF 5.3 3区 心理学 Q1 Mathematics Pub Date : 2024-05-01 Epub Date: 2024-02-13 DOI: 10.1080/00273171.2023.2289058
Jonathan J Park, Sy-Miin Chow, Sacha Epskamp, Peter C M Molenaar

Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.

近年来,出现了一类 "特异推理 "方法,以弥补提名推理和特异推理之间的差距。这些方法通过汇集跨个体的个体内信息来为群体层面的推断提供信息,反之亦然,从而描述特异过程中的提名趋势。目前的工作引入了一种新颖的 "特异性 "模型:分组链图向量自回归(scGVAR)。scGVAR 的独特之处在于它能够识别在滞后效应(1)和同期效应中具有共同动态网络结构的个体子群。蒙特卡洛模拟结果表明,当个体集群的同期动态存在差异时,scGVAR 有望超越类似方法,并在检测细微群体差异方面显示出更高的灵敏度,同时保持较低的类型一误差率。相比之下,一种与之竞争的方法--交替最小二乘法 VAR(ALS VAR)--在组间距离较大的情况下表现良好。本文还就 ALS VAR 和 scGVAR 在实际数据中的应用以及这两种方法的优势和局限性做了进一步的探讨。
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引用次数: 0
Correcting Regression Coefficients for Collider Bias in Psychological Research. 纠正心理学研究中对撞机偏差的回归系数。
IF 5.3 3区 心理学 Q1 Mathematics Pub Date : 2024-05-01 Epub Date: 2024-02-23 DOI: 10.1080/00273171.2024.2310418
Sophia J Lamp, David P MacKinnon
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引用次数: 0
Understanding Ability and Reliability Differences Measured with Count Items: The Distributional Regression Test Model and the Count Latent Regression Model. 理解用计数项目测量的能力和可靠性差异:分布回归测试模型和计数潜回归模型。
IF 3.8 3区 心理学 Q1 Mathematics Pub Date : 2024-05-01 Epub Date: 2024-02-13 DOI: 10.1080/00273171.2023.2288577
Marie Beisemann, Boris Forthmann, Philipp Doebler

In psychology and education, tests (e.g., reading tests) and self-reports (e.g., clinical questionnaires) generate counts, but corresponding Item Response Theory (IRT) methods are underdeveloped compared to binary data. Recent advances include the Two-Parameter Conway-Maxwell-Poisson model (2PCMPM), generalizing Rasch's Poisson Counts Model, with item-specific difficulty, discrimination, and dispersion parameters. Explaining differences in model parameters informs item construction and selection but has received little attention. We introduce two 2PCMPM-based explanatory count IRT models: The Distributional Regression Test Model for item covariates, and the Count Latent Regression Model for (categorical) person covariates. Estimation methods are provided and satisfactory statistical properties are observed in simulations. Two examples illustrate how the models help understand tests and underlying constructs.

在心理学和教育学中,测验(如阅读测验)和自我报告(如临床问卷)会产生计数,但与二进制数据相比,相应的项目反应理论(IRT)方法还不够完善。最近的进展包括双参数康威-麦克斯韦-泊松模型(2PCMPM),它是对拉施的泊松计数模型的推广,具有特定项目的难度、区分度和离散度参数。解释模型参数的差异可为项目构建和选择提供信息,但却很少受到关注。我们介绍了两个基于 2PCMPM 的解释性计数 IRT 模型:针对项目协变量的分布回归测试模型和针对(分类)人协变量的计数潜回归模型。提供了估计方法,并通过模拟观察到了令人满意的统计特性。两个例子说明了这些模型如何帮助理解测验和基本结构。
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引用次数: 0
Investigating Moderation Effects at the Within-Person Level Using Intensive Longitudinal Data: A Two-Level Dynamic Structural Equation Modelling Approach in Mplus. 利用密集纵向数据调查人内水平的调节效应:Mplus 中的两级动态结构方程建模方法》(A Two-Level Dynamic Structural Equation Modelling Approach in Mplus)。
IF 3.8 3区 心理学 Q1 Mathematics Pub Date : 2024-05-01 Epub Date: 2024-02-14 DOI: 10.1080/00273171.2023.2288575
Lydia Gabriela Speyer, Aja Louise Murray, Rogier Kievit

Recent technological advances have provided new opportunities for the collection of intensive longitudinal data. Using methods such as dynamic structural equation modeling, these data can provide new insights into moment-to-moment dynamics of psychological and behavioral processes. In intensive longitudinal data (t > 20), researchers often have theories that imply that factors that change from moment to moment within individuals act as moderators. For instance, a person's level of sleep deprivation may affect how much an external stressor affects mood. Here, we describe how researchers can implement, test, and interpret dynamically changing within-person moderation effects using two-level dynamic structural equation modeling as implemented in the structural equation modeling software Mplus. We illustrate the analysis of within-person moderation effects using an empirical example investigating whether changes in spending time online using social media affect the moment-to-moment effect of loneliness on depressive symptoms, and highlight avenues for future methodological development. We provide annotated Mplus code, enabling researchers to better isolate, estimate, and interpret the complexities of within-person interaction effects.

最近的技术进步为收集大量纵向数据提供了新的机会。利用动态结构方程建模等方法,这些数据可以为心理和行为过程的瞬间动态变化提供新的见解。在密集纵向数据(t > 20)中,研究人员通常会提出一些理论,暗示个体内部不同时刻发生变化的因素起着调节作用。例如,一个人的睡眠不足程度可能会影响外部压力对情绪的影响程度。在此,我们将介绍研究人员如何利用结构方程建模软件 Mplus 中的两级动态结构方程模型来实现、测试和解释动态变化的人内调节效应。我们通过一个实证例子来说明人内调节效应的分析,该例子调查了使用社交媒体上网时间的变化是否会影响孤独感对抑郁症状的瞬间效应,并强调了未来方法论发展的途径。我们提供了带注释的 Mplus 代码,使研究人员能够更好地分离、估计和解释复杂的人际互动效应。
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引用次数: 0
Approaches to Item-Level Data with Cross-Classified Structure: An Illustration with Student Evaluation of Teaching. 处理具有交叉分类结构的项目级数据的方法:以学生对教学的评价为例。
IF 3.8 3区 心理学 Q1 Mathematics Pub Date : 2024-05-01 Epub Date: 2024-02-13 DOI: 10.1080/00273171.2023.2288589
Sijia Huang

Student evaluation of teaching (SET) questionnaires are ubiquitously applied in higher education institutions in North America for both formative and summative purposes. Data collected from SET questionnaires are usually item-level data with cross-classified structure, which are characterized by multivariate categorical outcomes (i.e., multiple Likert-type items in the questionnaires) and cross-classified structure (i.e., non-nested students and instructors). Recently, a new approach, namely the cross-classified IRT model, was proposed for appropriately handling SET data. To inform researchers in higher education, in this article, the cross-classified IRT model, along with three existing approaches applied in SET studies, including the cross-classified random effects model (CCREM), the multilevel item response theory (MLIRT) model, and a two-step integrated strategy, was reviewed. The strengths and weaknesses of each of the four approaches were also discussed. Additionally, the new and existing approaches were compared through an empirical data analysis and a preliminary simulation study. This article concluded by providing general suggestions to researchers for analyzing SET data and discussing limitations and future research directions.

在北美的高等教育机构中,学生教学评价(SET)问卷被广泛应用于形成性和总结性教学评价。从 SET 问卷中收集的数据通常是具有交叉分类结构的项目级数据,其特点是多变量分类结果(即问卷中有多个李克特类型的项目)和交叉分类结构(即非嵌套的学生和教师)。最近,有人提出了一种新方法,即交叉分类 IRT 模型,用于适当处理 SET 数据。为了给高等教育研究人员提供参考,本文回顾了交叉分类 IRT 模型以及应用于 SET 研究的三种现有方法,包括交叉分类随机效应模型 (CCREM)、多层次项目反应理论 (MLIRT) 模型和两步综合策略。还讨论了这四种方法各自的优缺点。此外,还通过实证数据分析和初步模拟研究对新方法和现有方法进行了比较。文章最后为研究人员提供了分析 SET 数据的一般建议,并讨论了局限性和未来研究方向。
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引用次数: 0
The Forgotten Trade-off between Internal Consistency and Validity. 被遗忘的内部一致性与有效性之间的权衡。
IF 3.8 3区 心理学 Q1 Mathematics Pub Date : 2024-05-01 Epub Date: 2024-02-15 DOI: 10.1080/00273171.2024.2310429
Kayla M Garner
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引用次数: 0
期刊
Multivariate Behavioral Research
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