I have time to event data with clustered observations, so I am using proc phreg like so: proc phreg data = xxx covs(aggregate);      by byvar;      class cluster category;      model month * status(0) = pred cluster category / ties = efron;      id cluster; run; My problem is that when I run this model, I get this output: Testing Global Null Hypothesis: BETA=0 Test                    Chi-Square       DF Pr > ChiSq Likelihood Ratio          214.2638       32         <.0001 Score (Model-Based)       375.3325       32         <.0001 Score (Sandwich)           21.0000       21         0.4589 Wald (Model-Based)        231.4213       32         <.0001 Wald (Sandwich)         1.13909E11       21         <.0001 The Wald(Sandwich) Chi-Square is huge and significant; the Score(Sandwich) is small and not anywhere near significant. Is it possible there's something wrong with the Score(Sandwich)? Or the Wald(Sandwich)? Can anybody help with interpretation here? By the way, I initially had problems with this model getting a divide-by-zero error for one of the two bygroups when I used "/ ties = exact". I switched to "/ ties = efron", which does not give me problems. Still, I wonder if this means I have problematic patterns in the data that could be responsible for the widely divergent test statistics above. Also, FWIW, I was wondering if this discrepancy had anything to do with the inclusion of the cluster variable (which has roughly n = 20 categories) in the model. Indeed, removing the variable from the model statement substantially reduces the size of the Wald(Sandwich) chi-square (which remains significant) while cutting the p-value of the Score(Sandwich) by about 75% (which leaves it still non-significant). Answers, suggestions, and questions all welcome. ... View more