When I solve the semi-repeated crossover design data according to the procedures of FDA equivalence guidelines, I find that sometimes the hessian is not positive definite or G is not positive definite. In most cases, the hessian is not positive definite quite frequently. I know it could be because the dataset is not enough, the model is too complex, such as multicollinearity, or lack of information in certain directions of the parameter space.Based on some advice on the Internet and mixed of sas documents, I would use parms statements such as: parms 0.1,0.1,0.1,0.1,0.1/ lowerb=1e-10,.,1e-10; Sometimes we need to adjust the initial values, otherwise the hessian will still be non-positive. I would like to ask whether the model needs to adjust or ignore the information when the hessian is non-positive definite. In general, how to adjust to eliminate this note, do I need to adjust the initial iteration value over and over again? The following is my program, I hope to get some suggestions: proc mixed data=pk; class sequence subject period formulation; model lncmax= sequence period formulation/ ddfm=satterth singular=1e-10; random formulation/type=FA0(2) SUB=subject G ; repeated/ grp=formulation sub=subject ; parms 0.1,0.1,0.1,0.1,0.1/ lowerb=1e-10,.,1e-10; estimate 'T vs. R' formulation -1 1/cl alpha=0.1; lsmeans formulation / pdiff=control("R") alpha=0.1 cl; run;
... View more
