Parameter estimates

The treatment effect (the difference of the linear predictor between the two treatment levels; that is, the coefficient for [treatment=1]) is still not statistically significant, but only suggestive that treatment A [treatment=0] may be better than B [treatment=1] because the parameter estimate for treatment B is larger than that for A, and is thus associated with an increased probability of recurrence in the first 12 months. The linear predictor, (intercept + treatment effect) is an estimate of log(−log(1−P(recur_12,t)), where P(recur_{12, t}) is the probability of recurrence at 12 months for treatment t(=A or B). These predicted probabilities are generated for each observation in the dataset.