For the alcohol onset variable, we model the time to an event (Singer & Willett, 2003; Yamaguchi, 1991). The proportional odds model (Cox, 1972), also called Cox regression, is an appropriate approach. It is a multiplicative model: for a given explanatory variable s, a 1-unit increase in s implies an increase in the hazard of a nondrinking individual i to start drinking at a given time by a factor of eα, where α is the statistical parameter (weight) associated with variable s. For static networks, proportional odds models incorporating influence from connected alters have been discussed by, for example, Strang and Tuma (1993) and Valente (2005). The integration of a proportional odds time-to-event model with a SABM for network dynamics, as used here, is new. Greenan (2013) has shown that this combination can be made by specifying that the behavioral dependent variable in the SABM (here, the binary variable alcohol onset) is nondecreasing, and that risk predictor variables are included in the so-called behavioral rate function (Ripley et al., 2012), which specifies the relative rate at which the event (alcohol
