Dear lkeyes, Many thanks for your detailed answer, which was very helpful, even though I am disappointed by the results. When I run the 2-level unconditional model (without predictors), I have the following covariance parameter estimates: intercept estimate 0.12, StdErr 0.13. Values for the estimate and StdErr of each center's intercept seem to be correctly displayed In the "solution for random effects" table. DF=409. Thanks to the addition of the "parms /ParmsData= Parms;" statement, I am now able to obtain convergence of the algorithm for the 2-level model containing only a random intercept and the continuous thrombus_length variable (fixed slope). However, in the "solution for random effects", the estimate and StdErr of each center's intercept are now equal to zero. I also realized that in the 3-level model described in my earlier post, the estimate and StdErr of each center's intercept (random effects) are also equal to zero. Every time the thrombus_length variable is entered in a hierarchical logistic model, this problem occurs. Rescaling the variable [0 , 1] or making it categorical (tertiles) did not change the situation. This is the only variable leading to this problem. Unfortunately, it is the most important predictor and therefore it cannot be simply ruled out of any model. Could you please confirm that both models are invalid ? Or equivalent to a simple (non-hierarchical) logistic regression ? Is this due to the fact that the number of events (recanalisation=yes) per center is too small ? Thank you for your response. Guillaume

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