About Sara_p-value

Sara_p-value

Hi, I'm analyzing interaction between a categorical variable (3 levels: inadequate/adequate(ref)/excessive) and a binary medical condition on a binary outcome using modified Poisson regression with GEE. I recoded my exposures into a joint variable with a common reference category: Joint Exposure Categories: 0: Adequate + No medical condition (reference) 1: Adequate + Medical condition 2: Inadequate + No medical condition 3: Inadequate + Medical condition 4: Excessive + No medical condition 5: Excessive + Medical condition Current Model: proc genmod data=mydata; class joint (ref='0') id [other covariates]; model outcome = joint [covariates] / dist=poisson link=log; repeated subject=id / type=exch corrw; run; I want to Calculate RERI with valid confidence intervals for both exposure levels: RERI_inadequate = RR₃ - RR₂ - RR₁ + 1 RERI_excessive = RR₅ - RR₄ - RR₁ + 1 I can extract point estimates using: estimate 'RR01' joint 1 0 0 0 0; estimate 'RR10_Inad' joint 0 1 0 0 0; estimate 'RR11_Inad' joint 0 0 1 0 0; estimate 'RR10_Exc' joint 0 0 0 1 0; estimate 'RR11_Exc' joint 0 0 0 0 1; ods output Estimates=rr_out; Then manually calculate RERI point estimates. However, this doesn't provide confidence intervals for RERI. Questions: Is my ESTIMATE statement approach correct for extracting the individual RRs? What's the best method to get confidence intervals for RERI and how to code?

Sara_p-value

Hi, I'm analyzing interaction between a categorical variable (3 levels: inadequate/adequate(ref)/excessive) and a binary medical condition on a binary outcome using modified Poisson regression with GEE. I recoded my exposures into a joint variable with a common reference category: Joint Exposure Categories: 0: Adequate + No medical condition (reference) 1: Adequate + Medical condition 2: Inadequate + No medical condition 3: Inadequate + Medical condition 4: Excessive + No medical condition 5: Excessive + Medical condition Current Model: proc genmod data=mydata; class joint (ref='0') id [other covariates]; model outcome = joint [covariates] / dist=poisson link=log; repeated subject=id / type=exch corrw; run; I want to Calculate RERI with valid confidence intervals for both exposure levels: RERI_inadequate = RR₃ - RR₂ - RR₁ + 1 RERI_excessive = RR₅ - RR₄ - RR₁ + 1 I can extract point estimates using: estimate 'RR01' joint 1 0 0 0 0; estimate 'RR10_Inad' joint 0 1 0 0 0; estimate 'RR11_Inad' joint 0 0 1 0 0; estimate 'RR10_Exc' joint 0 0 0 1 0; estimate 'RR11_Exc' joint 0 0 0 0 1; ods output Estimates=rr_out; Then manually calculate RERI point estimates. However, this doesn't provide confidence intervals for RERI. Questions: Is my ESTIMATE statement approach correct for extracting the individual RRs? What's the best method to get confidence intervals for RERI and how to code?

Sara_p-value · ‎02-12-2025

I should have included my data structure in my original post - I'll make sure to do that next time. Thank you for your detailed and helpful response! Your explanation about the numerical computation issues with large year values really clarified my problem. I assume that it would make more sense to use 0, 1, 2, 3 as year values then.

Sara_p-value · ‎02-11-2025

I'm running a PROC GEE model in SAS with a multinomial outcome and using year as a predictor. PROC GEE DATA=data; CLASS id outcome(ref="2"); MODEL outcome = year / DIST=MULT LINK=GLOGIT; REPEATED SUBJECT=id/ TYPE=IND; RUN; When I use year as 2001, 2002, 2003, etc., the model does not provide p-values and Z statistics for year but I get estimates. However, when I recode year as 0, 1, 2, etc., firstly the estimates change, and now I also get p-values and Z statistics. I expected that only the intercept would shift, but why does this affect significance testing? Is there something about scale or estimation in GEE that I should consider?

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