Advancing Analytic Approaches to Address Key Questions in Mechanisms of Behavior Change Research

Abstract

Interest in studying mechanisms of behavior change (MOBCs) in substance use disorder (SUD) treatments has grown considerably in the past two decades. Much of this work has focused on identifying which variables statistically mediate the effect of SUD treatments on clinical outcomes. However, a fuller conceptualization of MOBCs will require greater understanding of questions that extend beyond traditional mediation analysis, including better understanding of when MOBCs change during treatment, when they are most critical to aiding the initiation or maintenance of change, and how MOBCs themselves arise as a function of treatment processes. Method: In the present study, we review why these MOBC-related questions are often minimally addressed in empirical research and provide examples of data analytic methods that may address these issues. We highlight several recent studies that have used such methods and discuss how these methods can provide unique theoretical insights and actionable clinical information. Results: Several statistical approaches can enhance the fi eld's understanding of the timing and development of MOBCs, including growthcurve modeling, time-varying effect modeling, moderated mediation analysis, dynamic systems modeling, and simulation methods. Conclusions: Adopting greater diversity in methods for modeling MOBCs will help researchers better understand the timing and development of key change variables and will expand the theoretical precision and clinical impact of MOBC research. Advances in research design, measurement, and technology are key to supporting these advances.

Effects of Initiating Abstinence from Alcohol on Daily Craving and Negative Affect: Results from a Pharmacotherapy Clinical Trial

“…There is growing interest in more precisely delineating how and when key clinical improvements occur during AUD treatment (Hallgren et al, in press; Huebner & Tonigan, 2007; Longabaugh et al, 2005; Magill et al, 2015). The present study evaluated the timing and magnitude of change in daily craving and negative affect in relationship to the initiation of abstinence from alcohol in the context of a trial of prazosin treatment for AUD.…”

“…The present study also extends this previous work by evaluating changes in daily negative affect (versus only craving) and by studying these changes in the context of a pharmacotherapy trial with no behavioral intervention (versus cognitive-behavioral treatments that include substantial education and psychotherapy around craving and negative affect). These findings could potentially help advance a greater understanding of the timing and magnitude of change in hypothesized mechanisms of behavioral change and key AUD treatment targets (Hallgren et al, in press), and may also help providers and patients better anticipate the expected timing and degree of change in the intensity of craving and negative affect, both over time and in relationship with initiating abstinence, providing evidence-based information that can support treatment goals and clinical decision-making.…”

“…These fears are clinically relevant, as they could lead to some hesitation to initiate abstinence due to worries about worsening craving and negative affect that create negative expectancies about abstinence goals. In contrast, having empirical information about the timing and degree of change in craving and negative affect that are expected to occur when one initiates abstinence could help guide patients in setting treatment goals about drinking and abstinence (Hallgren et al, in press), potentially creating more positive expectancies about treatment and abstinence.…”

Sensitivity Analysis of the No-Omitted Confounder Assumption in Latent Growth Curve Mediation Models

“…Latent growth curve mediation models (LGCMM) have become more common in applied contexts to study whether some treatment variable is expected to affect an outcome via changes in some mediating process. Given the growing popularity of this model in the field (see Hallgren et al, 2018), it is critically important to check underlying assumptions to ensure valid results. One of these assumptions is an untestable assumption that there are not omitted confounders from the model.…”

“…One important application of a multiple mediator model with two covarying mediators is the latent growth curve mediation model (LGCMM) with repeated measures/multilevel data, whereby some treatment variable is expected to affect an outcome via the change in some mediating process (Selig & Preacher, 2009; von Soest & Hagtvet, 2011). LGCMM is increasingly used to test hypothesized mechanisms of behavior change following treatments for psychological disorders (Hallgren, Wilson, & Witkiewitz, 2018). For example, as shown in Figure 1, in alcohol treatment one might hypothesize that an anti-craving medication (e.g., naltrexone) affects reductions in alcohol use via changes in craving over time.…”