Meeting the Bare Minimum: Quality Assessment of Idiographic Temporal Networks Using Power Analysis and Predictive Accuracy Analysis

The network theory of psychopathology inspired clinicians and researchers to use idiographic networks to study how symptoms of an individual interact over time, hoping to find the target symptom(s) for intervention to most effectively break this self-sustaining network. These networks are often based on the vector autoregressive (VAR) model and rely on intensive longitudinal data collected in patients’ daily lives. Nowadays, one major challenge these networks are faced with is that they are used without sufficient quality assessments. Since VAR-based temporal networks are complex and highly parameterized, they can easily face problems of low statistical power and overfitting, especially when the time-series available is short. In this study, we review existing idiographic network studies with a focus on the number of variables and time-points used in the analysis, showing that the "big network, short time-series" problem is prevalent. As potential solutions, we propose two simulation-based methods that aim to find the optimal number of time-points to be collected: power analysis and predictive accuracy analysis. Two applications of both methods are demonstrated: (1) "a-priori"---informing the sample size planning of future network studies, and (2) "retrospective"---evaluating whether the sample size of existing network studies was large enough to avoid problems of low statistical power and overfitting. Results confirmed the observation that the sample sizes in past network studies are often insufficient, suggesting that findings of existing network studies should be critically assessed. Future idiographic network studies are thus strongly advised to make more guided decisions on sample size using the proposed methods.