The Influence of Autoregressive Relation Strength and Search Strategy on Directionality Recovery in Group Iterative Multiple Model Estimation

Unified structural equation modeling (uSEM) implemented in the group iterative multiple model estimation (GIMME) framework has recently been widely used for characterizing within-person network dynamics of behavioral and functional neuroimaging variables. Previous studies have established that GIMME accurately recovers the presence of relations between variables. However, recovery of relation directionality is less consistent, which is concerning given the importance of directionality estimates for many research questions. There is evidence that strong autoregressive relations may aid directionality recovery and indirect evidence that a novel version of GIMME allowing for multiple solutions could improve recovery when such relations are weak, but it remains unclear how these strategies perform under a range of study conditions. Using comprehensive simulations that varied the strength of autoregressive relations among other factors, this study evaluated the directionality recovery of two GIMME search strategies: (a) estimating autoregressive relations by default in the null model (GIMME-AR) and (b) generating multiple solution paths (GIMME-MS). Both strategies recovered directionality best-and were roughly equivalent in performance-when autoregressive relations were strong (e.g., beta = .60). When they were weak (beta <= .10), GIMME-MS displayed an advantage, although overall directionality recovery was modest. Analyses of empirical data in which autoregressive relations were characteristically strong (resting state functional MRI) versus weak (daily diary) mirrored simulation results and confirmed that these strategies can disagree on directionality when autoregressive relations are weak. Findings have important implications for psychological and neuroimaging applications of uSEM/GIMME and suggest specific scenarios in which researchers might or might not be confident in directionality results. Translational Abstract Network modeling methods such as group iterative multiple model estimation (GIMME) can provide valuable insights into person-specific processes present in time series data from multiple domains, including functional neuroimaging and intensive longitudinal studies of psychological variables. In these applications, the directionality of contemporaneous relations (those in which one variable predicts another at the same time point) is often of interest. However, the directionality of these relations is difficult to estimate in practice, and it is unclear what methods or features of data may facilitate accurate estimates. In this study, we assessed the ability of two GIMME variants to estimate directionality in data sets in which autoregressive relations (those in which variables predict themselves at the next time point) are strong versus weak. The first variant (GIMME-AR) relies on strong autoregressive relations to inform directionality during model estimation, while the second variant (GIMME-MS) estimates multiple models with relations in opposite directions and allows these models to be compared after estimation with standard fit indices. We found that both methods performed best, and often provided similar directionality estimates, when autoregressive relations in data were strong (e.g., beta = .60). When autoregressive relations were weak (beta <= .10), GIMME-MS displayed a slight advantage over GIMME-AR, but overall performance was modest. These results indicate that GIMME-AR is likely preferable to GIMME-MS for analyzing data sets with strong autoregressive relations, such as functional neuroimaging data, while GIMME-MS may be preferable for analyzing data sets with weak autoregressive relations, such as daily diary or ambulatory assessment data.

Automated summary

This study assessed the ability of two variants of GIMME to estimate directionality in datasets with strong versus weak autoregressive relations. The results showed that both methods performed best when autoregressive relations were strong, with GIMME-AR being slightly preferable for datasets with strong autoregressive relations and GIMME-MS being slightly preferable for datasets with weak autoregressive relations.