Hi friends, I am trying to get the adjusted prevalence ratios for the use of e-cig use (vs non-users) by the exposure to poor mental health in the past 30 days. The outcome is QN35 (current e-cig use coded 1 for those used it in the past 30 days vs. 2 for non-users). The exposure variable is QN85 (mental health is poor in the past 30 days: 1 - yes, 2-no). I am trying to estimate the adjusted prevalence ratio (aPR) for the use of e-cig by poor mental health while adjusting for socio-demographic variables (listed in the CLASS statement). I am modeling the use of e-cig (QN35=1) but the aPR that I get does not make sense. I mean I am expecting the aPR to be above 1 (poor mental health is associated with higher risk to use e-cig) but I am getting an aPR below 1, which is not what expected and is contradicting the direction of the OR obtained from the logistic regression modeling. All estimates I got are in the opposite direction to what is expected. When I switch to (-1) in the ESTIMATE statement (instead of 1), then I get the expected aPR (above 1). I guess I am having trouble figuring out the coding in the ESTIMATE statement and how to correctly do that. The design variable output for the exposure variable (QN85) in SAS shows that 1 (Yes) is coded as 1 while 2 (No) is coded as 0. So, when I put 1 in the ESTIMATE statement, I guess 0 is the reference (aPR for Yes vs No) but if I switch to -1, then I am estimating the aPR for No vs Yes? I am completely confused and I have no idea how to fix the code to get appropriate aPR that make sense. Any help is appreciated. The code I am using is below. TIA.     PROC GENMOD DATA=NEW ; CLASS PSU STRATUM QN85 (REF='2') Q2 (REF='Male') NEWRACE (REF='WHITE') NEWGRADE (REF='9TH GRADE') SEXORIENTATION (REF='HETEROSEXUAL') INSTRMDL (REF='Other')/PARAM=REF; WEIGHT WEIGHT; MODEL QN35(event='1') = QN85 Q2 NEWRACE NEWGRADE SEXORIENTATION INSTRMDL/ Dist=poisson Link=log type3; ESTIMATE 'RR CURRENT MENTAL HEALTH NOT GOOD YES vs. NO' QN85 1/EXP; REPEATED SUBJECT = PSU(STRATUM); RUN;   ... View more

Hi friends, I am trying to get the adjusted prevalence ratios for the use of e-cig use (vs non-users) by the exposure to poor mental health in the past 30 days. The outcome is QN35 (current e-cig use coded 1 for those used it in the past 30 days vs. 2 for non-users). The exposure variable is QN85 (mental health is poor in the past 30 days: 1 - yes, 2-no). I am trying to estimate the adjusted prevalence ratio (aPR) for the use of e-cig by poor mental health while adjusting for socio-demographic variables (listed in the CLASS statement). I am modeling the use of e-cig (QN35=1) but the aPR that I get does not make sense. I mean I am expecting the aPR to be above 1 (poor mental health is associated with higher risk to use e-cig) but I am getting an aPR below 1, which is not what expected and is contradicting the direction of the OR obtained from the logistic regression modeling. All estimates I got are in the opposite direction to what is expected. When I switch to (-1) in the ESTIMATE statement (instead of 1), then I get the expected aPR (above 1). I guess I am having trouble figuring out the coding in the ESTIMATE statement and how to correctly do that. The design variable output for the exposure variable (QN85) in SAS shows that 1 (Yes) is coded as 1 while 2 (No) is coded as 0. So, when I put 1 in the ESTIMATE statement, I guess 0 is the reference (aPR for Yes vs No) but if I switch to -1, then I am estimating the aPR for No vs Yes? I am completely confused and I have no idea how to fix the code to get appropriate aPR that make sense. Any help is appreciated. The code I am using is below. TIA.     PROC GENMOD DATA=NEW ; CLASS PSU STRATUM QN85 (REF='2') Q2 (REF='Male') NEWRACE (REF='WHITE') NEWGRADE (REF='9TH GRADE') SEXORIENTATION (REF='HETEROSEXUAL') INSTRMDL (REF='Other')/PARAM=REF; WEIGHT WEIGHT; MODEL QN35(event='1') = QN85 Q2 NEWRACE NEWGRADE SEXORIENTATION INSTRMDL/ Dist=poisson Link=log type3; ESTIMATE 'RR CURRENT MENTAL HEALTH NOT GOOD YES vs. NO' QN85 1/EXP; REPEATED SUBJECT = PSU(STRATUM); RUN; ... View more