The Impact of Sparse Datasets When Harmonizing Data from Studies with Different Measures of the Same Construct.

IF 3 2区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Prevention Science Pub Date : 2024-08-01 Epub Date: 2024-07-18 DOI:10.1007/s11121-024-01704-8
George W Howe, Getachew Dagne, Alberto Valido, Dorothy L Espelage, Karen M Abram, C Hendricks Brown, Carlos Gallo
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Abstract

Prevention science has increasingly turned to integrative data analysis (IDA) to combine individual participant-level data from multiple studies of the same topic, allowing us to evaluate overall effect size, test and model heterogeneity, and examine mediation. Studies included in IDA often use different measures for the same construct, leading to sparse datasets. We introduce a graph theory method for summarizing patterns of sparseness and use simulations to explore the impact of different patterns on measurement bias within three different measurement models: a single common factor, a hierarchical model, and a bifactor model. We simulated 1000 datasets with varying levels of sparseness and used Bayesian methods to estimate model parameters and evaluate bias. Results clarified that bias due to sparseness will depend on the strength of the general factor, the measurement model employed, and the level of indirect linkage among measures. We provide an example using a synthesis dataset that combined data on youth depression from 4146 youth who participated in 16 randomized field trials of prevention programs. Given that different synthesis datasets will embody different patterns of sparseness, we conclude by recommending that investigators use simulation methods to explore the potential for bias given the sparseness patterns they encounter.

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在协调同一结构的不同测量方法的研究数据时,稀疏数据集的影响。
预防科学已越来越多地转向综合数据分析(IDA),将来自同一主题多项研究的个体参与者层面的数据结合起来,使我们能够评估总体效应大小、测试和模拟异质性并检查中介作用。包含在 IDA 中的研究通常对同一构念使用不同的测量方法,从而导致数据集稀疏。我们介绍了一种总结稀疏模式的图论方法,并使用模拟来探讨三种不同测量模型中不同模式对测量偏差的影响:单一公共因子、分层模型和双因子模型。我们模拟了 1000 个具有不同稀疏程度的数据集,并使用贝叶斯方法估计模型参数和评估偏差。结果表明,稀疏性导致的偏差将取决于一般因素的强度、所采用的测量模型以及测量之间的间接联系水平。我们以一个综合数据集为例进行了说明,该数据集综合了 4146 名青少年的青少年抑郁症数据,这些青少年参加了 16 个预防项目的随机实地试验。鉴于不同的综合数据集会体现出不同的稀疏性模式,我们最后建议研究人员使用模拟方法来探索他们所遇到的稀疏性模式中可能存在的偏差。
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来源期刊
Prevention Science
Prevention Science PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
6.50
自引率
11.40%
发文量
128
期刊介绍: Prevention Science is the official publication of the Society for Prevention Research. The Journal serves as an interdisciplinary forum designed to disseminate new developments in the theory, research and practice of prevention. Prevention sciences encompassing etiology, epidemiology and intervention are represented through peer-reviewed original research articles on a variety of health and social problems, including but not limited to substance abuse, mental health, HIV/AIDS, violence, accidents, teenage pregnancy, suicide, delinquency, STD''s, obesity, diet/nutrition, exercise, and chronic illness. The journal also publishes literature reviews, theoretical articles, meta-analyses, systematic reviews, brief reports, replication studies, and papers concerning new developments in methodology.
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