Thank you for your reply! I am testing only one hypothesis, the effect of jsh on the outcome surv, shown in the full model bellow: proc glimmix method = laplace data=finale_updated; class year; model surv (event = '1')= jsh /S dist = binary link = logit ddfm =bw; random int/ subject = year; covtest 'var (year)= 0' 0 / est parms ; run; Here is the output for the full model: Solutions for Fixed EffectsEffect Estimate StandardError DF t Value Pr > |t|InterceptJSH -2.7900 0.5244 8 -5.32 0.0007 -0.6452 0.4951 2789 -1.30 0.1926 Fit Statistics-2 Log LikelihoodAIC (smaller is better)AICC (smaller is better)BIC (smaller is better)CAIC (smaller is better)HQIC (smaller is better) 846.75 852.75 852.76 853.34 856.34 851.48 And for the empty model, without the variable jsh Fit Statistics-2 Log LikelihoodAIC (smaller is better)AICC (smaller is better)BIC (smaller is better)CAIC (smaller is better)HQIC (smaller is better) 2170.83 2174.83 2174.84 2175.23 2177.23 2173.98 By comparing the two outputs, you see that the p value for t is very high. However, the difference in -2 Log Likelihood is just as important, and all information criteria! I am wondering what may be causing this? What shall I conclude? How do I interpret the odds ratio, not shown here, but there is a negative relationship. Best, Boban
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