I am performing a retrospective study and the relative statistic analysis. I am studying the the risk factors for the occurrence of complications during medical procedures.
I have 50 subjects undergoing a total of 250 procedures. The number of procedures per subject may vary (2-10). I got 3 fixed effects for the subjects (sex, age, pathology_type), and 5 fixed effects (firstprocedure where painfulprocedure anestheticapproach respiratorysupport) for the procedures. I am running a GLMM on SPPS.
This is the code.
*Generalized Linear Mixed Models. GENLINMIXED /DATA_STRUCTURE SUBJECTS=@#paziente /FIELDS TARGET=complicatedprocedure TRIALS=NONE OFFSET=NONE /TARGET_OPTIONS REFERENCE='NO' DISTRIBUTION=BINOMIAL LINK=LOGIT /FIXED EFFECTS=sex age pathology_type firstprocedure where painfulprocedurepain anestheticapproach respiratorysupport USE_INTERCEPT=TRUE /RANDOM EFFECTS=@#paziente USE_INTERCEPT=TRUE COVARIANCE_TYPE=VARIANCE_COMPONENTS /BUILD_OPTIONS TARGET_CATEGORY_ORDER=ASCENDING INPUTS_CATEGORY_ORDER=ASCENDING MAX_ITERATIONS=100 CONFIDENCE_LEVEL=95 DF_METHOD=RESIDUAL COVB=ROBUST PCONVERGE=0.000001(ABSOLUTE) SCORING=0 SINGULAR=0.000000000001 /EMMEANS TABLES=sex COMPARE=sex CONTRAST=PAIRWISE /EMMEANS TABLES=pathology_type COMPARE=pathology_type CONTRAST=PAIRWISE /EMMEANS TABLES=where COMPARE=where CONTRAST=PAIRWISE /EMMEANS TABLES=painfulprocedurepain COMPARE=painfulprocedure CONTRAST=PAIRWISE /EMMEANS TABLES=anestheticapproach COMPARE=anestheticapproach CONTRAST=PAIRWISE /EMMEANS TABLES=respiratorysupport COMPARE=respiratorysupport CONTRAST=PAIRWISE /EMMEANS_OPTIONS SCALE=ORIGINAL PADJUST=SEQBONFERRONI.
I have a series of questions:
I do not clearly understand the warning below. I tried to look around, but it appears to me that this is a problem that may occur frequently and sometimes there is no major error in the statistical methods. By the way, the output is not weird at all. To the contrary, I pretty much have results fitting with expected.
glmm: The final Hessian matrix is not positive definite although all convergence criteria are satisfied. The procedure continues despite this warning. Subsequent results produced are based on the last iteration. Validity of the model fit is uncertain. Data Structure: One or more subject fields were specified but not actually used in the analysis.
So, in the end, when I receive such warning, what should I do? Does my data mean anything?
In the output, is Exp (Coefficient) the adjusted odds ratio?
In the case, if Exp (Coefficient) of A vs. B is 0.021, may I say that the Exp (Coefficient) of B vs. A is 1/0.021=47?