Causal Network Modeling of the Determinants of Drinking Behavior in Comorbid Alcohol Use and Anxiety Disorder

Abstract

Background: Anxiety and depression disorders (internalizing psychopathology) occur in approximately 50% of patients with alcohol use disorder (AUD) and mark a 2-fold increase in the rate of relapse in the months following treatment. In a previous study using network modeling, we found that perceived stress and drinking to cope (DTC) with negative affect were central to maintaining network associations between internalizing psychopathology INTP and drinking in comorbid individuals. Here, we extend this approach to a causal framework.

Methods: Measures of INTP, drinking urges/behavior, abstinence self-efficacy, and DTC were obtained from 362 adult AUD treatment patients who had a co-occurring anxiety disorder. Data were analyzed using a machine-learning algorithm ("Greedy Fast Causal Inference"[ GFCI]) that infers paths of causal influence while identifying potential influences associated with unmeasured ("latent") variables.

Results: DTC with negative affect served as a central hub for 2 distinct causal paths leading to drinking behavior, (i) a direct syndromic pathway originating with social anxiety and (ii) an indirect stress pathway originating with perceived stress.

Conclusions: Findings expand the field's knowledge of the paths of influence that lead from internalizing disorder to drinking in AUD as shown by the first application in psychopathology of a powerful network analysis algorithm (GFCI) to model these causal relationships.

Keywords: Alcohol Use Disorder; Anxiety; Comorbidity; Machine Learning; Network Analysis.

© 2018 by the Research Society on Alcoholism.

Figures

Figure 1.

Figure 1 illustrates four different ways that variables A, B, and C could be causally related to each other according to constraint-based causal reasoning. Figure 1 (a) shows a collider graph, where A causes B, C causes B, and there is no edge between A and C. In this graph, the following statistical independence statements are true: A is unconditionally independent of C, and A is dependent on C conditional on B. However if the C to B edge is reversed (e.g. Figure 1 (b)), or the A to B edge is reversed (Figure 1 (c)), or both are reversed (Figure 1 (d)), we would instead find that A is unconditionally dependent on C, and that A is independent of C conditional on B. As such, conditional independence tests can sometimes identify causal direction, e.g. by identifying whether data were generated from Figure 1 (a), or from one of the other three graphs shown in Figure 1. FCI leverages observations like this to orient causal edges, and to rule out or allow for the possibility of latents confounding the causal relationships.

Figure 2.

A partial ancestral graph (PAG) produced by the greedy fast casual inference algorithm depicting relationship magnitude (BIC values) between network elements in patients with INTP-AUD comorbidity. See Table 2 for an explanation of edge types.

Figure 3.

Removal of any other element (e.g., GAD, PAN, AGR, and SEL) did not result in changes to the core drinking pathway identified in Figure 3.