Mediation analysis with binary outcomes: Direct and indirect effects of pro-alcohol influences on alcohol use disorders

Abstract A risk factor or intervention (an independent variable) may influence a substance abuse outcome (the dependent variable) indirectly, by affecting an intervening variable (a mediator) that in turn affects that outcome. Mediation analysis is a statistical method commonly used to examine the interrelations among independent, mediating, and dependent variables to obtain the direct and indirect effects of an independent variable on a continuous dependent variable. However, mediation analysis may also be used with binary outcomes, such as a diagnosis of an alcohol use disorder (AUD). Study 1 demonstrated methods of mediation analysis with binary outcomes by examining the direct and indirect effects of pro-alcohol social influences on an AUD, as a function of: (a) the distribution of the independent variable (binary vs. continuous), (b) the frequency of the outcome (non-rare vs. rare), and (c) the effect metric (probability vs. odds ratio). Study 2 was a Monte Carlo (simulation) study of bias in the indirect effects based on estimates from the first study. These methods have wide applicability in addictions research because many key outcomes are binary, and mediation analysis is frequently used to study the causal mechanisms by which interventions and risk factors affect substance abuse.