{'fraction_non_alphanumeric': 0.01679212507237985, 'fraction_numerical': 0.0, 'mean_word_length': 5.6976744186046515, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Advancing Analytic Approaches to Address Key Questions in Mechanisms of Behavior Change Research.

OBJECTIVE
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 field's understanding of the timing and development of MOBCs, including growth-curve 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.