Using features of dynamic networks to guide treatment selection and outcome prediction: The central role of uncertainty

Multivariate time series models are commonly used in psychology to investigate person-specific associations between multiple variables. They are often represented and interpreted as dynamic network models, where features such as the centrality of nodes can potentially guide treatment selection and outcome prediction. Researchers typically rely on point estimates of specific network features while ignoring estimation uncertainty, which can lead to wrong inferences and over-optimistic claims. We introduce a one-step Bayesian approach for estimating multilevel vector autoregressive models (BmlVAR), which enables uncertainty quantification for person-specific network features and the regression of external outcomes on such features. In a preregistered simulation study, we compare the new model with several popular methods for network estimation. We also apply all methods to empirical data to highlight their differences. Our simulation results indicate that all methods perform mediocrely in estimating different centrality measures in practically relevant settings. BmlVAR still outperforms the other methods in many simulation conditions, especially regarding the statistical power to detect associations with an outcome. However, estimating the model properly can be challenging with limited data. Overall, all methods require a lot of data or very large effects to produce reasonably accurate results. Thus, although centrality measures based on dynamic networks have been widely used, our simulation study suggests they are unlikely to work well for achieving major goals of interest, such as guiding treatment selection and outcome prediction. We provide a new model that incorporates estimation uncertainty into the modelling process, thereby protecting against premature conclusions.