Dependence Graphs Based on Association Rules to Explore Delusional Experiences

Methods to estimate dependence graphs among variables, have quickly gained popularity in psychopathology research. To date, multiple methods have been proposed but recent studies report several drawbacks impacting on the validity of the conclusions as it is argued that assumptions and conditions underlying the methods commonly used and the nature of the data is lacking alignment. A particularly important issue is that underlying dynamics potentially present in heterogeneous datasets are disregarded, as the methods focus on the variables but not on individuals. This work also argues that the networks may lack relevant components as current methods ignore connections beyond pairwise interactions between individual symptoms. This study addresses these issues with a novel method for constructing dependence graphs based on applying Association Rules to binary records, which is often the type of records in the psychopathology domain. To demonstrate the benefits, we examine 12 delusional experiences in a sample of 1423 subjects with psychotic disorders. We show that by extracting Association Rules using an algorithm called apriori, in addition to facilitating an intuitive interpretation, previously unseen relevant dependencies are revealed from higher order interactions among psychotic experiences in subgroups of patients.