Michael I. Demidenko , Dominic P. Kelly , Felicia A. Hardi , Ka I. Ip , Sujin Lee , Hannah Becker , Sunghyun Hong , Sandra Thijssen , Monica Luciana , Daniel P. Keating
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When these topics are investigated, there is typically inconsistent operationalization of variables between studies which may be measuring different aspects of the environment and thus different associations in the analytic models. Multiverse analyses (<span>Steegen et al., 2016</span>) are an efficacious technique for investigating the effect of different operationalizations of the same construct on underlying interpretations. While one of the assets of <span>Thijssen et al. (2020)</span> was its large sample from the ABCD data, the authors used an early release that contained 38% of the full ABCD sample. Then, the analyses used several ‘researcher degrees of freedom’ (<span>Gelman and Loken, 2014</span>) to operationalize key independent, mediating and dependent variables, including but not limited to, the use of a latent factor of preadolescents' environment comprised of different subfactors, such as parental monitoring and child-reported family conflict. While latent factors can improve reliability of constructs, the nuances of each subfactor and measure that comprise the environment may be lost, making the latent factors difficult to interpret in the context of individual differences. This study extends the work of <span>Thijssen et al. (2020)</span> by evaluating the extent to which the analytic choices in their study affected their conclusions. In Aim 1, using the same variables and models, we replicate findings from the original study using the full sample in Release 3.0. Then, in Aim 2, using a multiverse analysis we extend findings by considering nine alternative operationalizations of family environment, three of puberty, and five of brain measures (total of 135 models) to evaluate the impact on conclusions from Aim 1. In these results, 90% of the directions of effects and 60% of the <em>p</em>-values (e.g. <em>p</em> > .05 and <em>p</em> < .05) across effects were comparable between the two studies. However, raters agreed that only 60% of the effects had replicated. Across the multiverse analyses, there was a degree of variability in beta estimates across the environmental variables, and lack of consensus between parent reported and child reported pubertal development for the indirect effects. This study demonstrates the challenge in defining which effects replicate, the nuance across environmental variables in the ABCD data, and the lack of consensus across parent and child reported puberty scales in youth.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. 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引用次数: 2
摘要
越来越多的证据表明,环境因素对大脑的发育有重大影响(Hyde et al., 2020;麦克尤恩和阿基尔,2020)。最近来自青少年大脑认知发展(ABCD)研究®的研究表明,青春期可能间接解释了家庭环境与大脑结构和功能之间的一些关联(Thijssen等人,2020)。然而,有限数量的大型研究已经评估了环境因素影响神经发育的内容、方式和原因。在调查这些主题时,通常在测量环境的不同方面的研究之间存在不一致的变量操作化,因此分析模型中的不同关联。多元宇宙分析(Steegen et al., 2016)是一种有效的技术,用于研究同一结构的不同操作方式对潜在解释的影响。虽然Thijssen等人(2020)的优势之一是其ABCD数据的大样本,但作者使用的早期版本包含了完整ABCD样本的38%。然后,分析使用了几个“研究者自由度”(Gelman和Loken, 2014)来操作关键的独立、中介和因变量,包括但不限于使用由不同子因素组成的青春期前环境的潜在因素,如父母监控和儿童报告的家庭冲突。虽然潜在因素可以提高构建的可靠性,但构成环境的每个子因素和测量的细微差别可能会丢失,使得潜在因素难以在个体差异的背景下解释。本研究通过评估其研究中的分析选择对其结论的影响程度,扩展了Thijssen等人(2020)的工作。在Aim 1中,使用相同的变量和模型,我们使用Release 3.0中的完整样本复制了原始研究的结果。然后,在目标2中,我们使用多元宇宙分析,通过考虑家庭环境的九种可选操作化,青春期的三种操作化和大脑测量的五种操作化(总共135种模型)来扩展研究结果,以评估对目标1结论的影响。在这些结果中,90%的效应方向和60%的p值(例如p >.05和p <0.05),两项研究的交叉效应具有可比性。然而,评级者一致认为,只有60%的影响是可复制的。在多元宇宙的分析中,在环境变量的beta估计中存在一定程度的可变性,并且在间接影响方面,父母报告和儿童报告的青春期发育之间缺乏共识。本研究表明,在定义哪些影响是可复制的、ABCD数据中环境变量之间的细微差别以及父母和儿童报告的青少年青春期量表缺乏共识方面存在挑战。
Mediating effect of pubertal stages on the family environment and neurodevelopment: An open-data replication and multiverse analysis of an ABCD Study®
Increasing evidence demonstrates that environmental factors meaningfully impact the development of the brain (Hyde et al., 2020; McEwen and Akil, 2020). Recent work from the Adolescent Brain Cognitive Development (ABCD) Study® suggests that puberty may indirectly account for some association between the family environment and brain structure and function (Thijssen et al., 2020). However, a limited number of large studies have evaluated what, how, and why environmental factors impact neurodevelopment. When these topics are investigated, there is typically inconsistent operationalization of variables between studies which may be measuring different aspects of the environment and thus different associations in the analytic models. Multiverse analyses (Steegen et al., 2016) are an efficacious technique for investigating the effect of different operationalizations of the same construct on underlying interpretations. While one of the assets of Thijssen et al. (2020) was its large sample from the ABCD data, the authors used an early release that contained 38% of the full ABCD sample. Then, the analyses used several ‘researcher degrees of freedom’ (Gelman and Loken, 2014) to operationalize key independent, mediating and dependent variables, including but not limited to, the use of a latent factor of preadolescents' environment comprised of different subfactors, such as parental monitoring and child-reported family conflict. While latent factors can improve reliability of constructs, the nuances of each subfactor and measure that comprise the environment may be lost, making the latent factors difficult to interpret in the context of individual differences. This study extends the work of Thijssen et al. (2020) by evaluating the extent to which the analytic choices in their study affected their conclusions. In Aim 1, using the same variables and models, we replicate findings from the original study using the full sample in Release 3.0. Then, in Aim 2, using a multiverse analysis we extend findings by considering nine alternative operationalizations of family environment, three of puberty, and five of brain measures (total of 135 models) to evaluate the impact on conclusions from Aim 1. In these results, 90% of the directions of effects and 60% of the p-values (e.g. p > .05 and p < .05) across effects were comparable between the two studies. However, raters agreed that only 60% of the effects had replicated. Across the multiverse analyses, there was a degree of variability in beta estimates across the environmental variables, and lack of consensus between parent reported and child reported pubertal development for the indirect effects. This study demonstrates the challenge in defining which effects replicate, the nuance across environmental variables in the ABCD data, and the lack of consensus across parent and child reported puberty scales in youth.