Actor-based analysis of peer influence in A Stop Smoking In Schools Trial (ASSIST)

As shown by the success of network intervention studies that exploit the occurrence of peer influence in their target group, the reliable assessment of peer influence processes can be important for informing public health policy and practice. A recently developed tool for assessing peer influence in longitudinal social network data is stochastic actor-based modeling. The body of the literature in which this method is applied is growing, but how reliable are the results? In this paper, we identify two shortcomings in this literature: the questionable assumption of temporal homogeneity, and the potential dependence of results on the inclusion of nuisance parameters in the model specification. These issues are resolved by analyzing the data of three schools selected from ASSIST, a large UK-based trial of a school-based smoking prevention intervention. Results show that the co-evolution of friendship and smoking is a time heterogeneous process, and that results are sensitive to specification details. However, the peer influence parameter is not affected by either, but emerges as surprisingly stable over time and robust to model variation. This establishes confidence in the method and encourages detailed future investigations of peer influence in ASSIST.