Parameter estimates
The treatment effect is still not statistically significant but only suggestive that treatment A may be better than B because the parameter estimate for treatment B is associated with an increased probability of recurrence in the first 12 months. The period values are statistically significantly different from 0, but this is because of the fact that an intercept term is not fit. The period effect (the difference between the values of the linear predictor for [period=1] and [period=2]) is not statistically significant, as can be seen in the tests of model effects. The linear predictor (period effect + treatment effect) is an estimate of log(−log(1−P(recurp, t)), where P(recurp, t) is the probability of recurrence at the period p(=1 or 2, representing six months or 12 months) given treatment t(=A or B). These predicted probabilities are generated for each observation in the dataset.