Online Program

Propensity Model Evaluation of the Effects of Online Social Network Participation on Promoting Smoking Cessation

Amanda L. Graham, Schroeder Institute for Tobacco Research and Policy Studies 
George Papandonatos, Brown University 
*Bahar Erar, Brown University 
Cassandra A. Stanton, Westat, Inc. 

Keywords: Causal inference, Generalized Boosted Models, Multiple Treatments, Smoking Cessation, Internet

The association between participation in online communities for cessation and abstinence, shown in prior observational studies, may be partly or entirely related to selection bias. This study examines the causal effects of participation in an online smoking cessation community on abstinence. Participants were N=492 adult current smokers in the enhanced Internet arm of The iQUITT Study, a randomized trial of Internet and telephone treatment for smoking cessation. Automated tracking metrics of passive and active community use were extracted from the site at three months. Self-selected community use defines the groups of interest: “None,” “Passive,” and “Both” (passive+active). Inverse probability of treatment weighting was used to correct for baseline imbalances on demographic, smoking, psychosocial, and medical history variables. Propensity weights estimated via generalized boosted models (GBM) were used to calculate Average Treatment Effects (ATE) and Average Treatment effects on the Treated (ATT), as defined in this multiple treatment setting. Results show that community users were more likely to quit smoking at three months than non-users. The estimated benefit from use of online community resources was even larger among subjects with high propensity to use them. Results suggest that lurking in online communities confers specific abstinence benefits.