The risk factors were examined collectively via forward selection in HLM growth curves. We used forward selection because it tends to be more accurate and conservative than backward elimination in selecting predictors (Derksen & Keselman, 1992). The stepAIC function of the MASS (Venables & Ripley, 2002) package in R determined the best set of predictors by selecting iteratively only those predictors that incrementally improved model fit, as measured by Akaike’s Information Criterion (AIC). The AIC balances the goodness of fit with the complexity of the model, by penalizing models with more predictors. The typical penalty for AIC is 2 times the number of parameters (Sheather, 2009), whereas we set the penalty to 4 times the number of parameters for a more conservative threshold for selecting predictors (Venables & Ripley, 2002). We kept a predictor if it was selected by forward selection in at least half of the imputed data sets (10/20). First, we selected predictors of the intercepts. Second, in a separate model, we selected predictors of the slopes. Third, we combined the predictors of the intercepts and slopes to
