I am using Stata 16 and I am trying to obtain the exponentiated form of my estimates with multiple imputation (mi estimate) using xtgee with the eform option, however, the estimates I obtain are the same as when there is no eform option.
Before using mi estimate, I used mi impute chained.
A simplified version of my code without eform is:
Code:
. mi estimate: xtgee vf gender age, family(binomial) link(logit) corr(exch) vce(robust) Multiple-imputation estimates Imputations = 30 GEE population-averaged model Number of obs = 496 Group variable: clinic_bsl~m Number of groups = 16 Link: logit Obs per group: Family: binomial min = 10 Correlation: exchangeable avg = 31.0 Scale parameter: 1 max = 64 Average RVI = 0.0000 Largest FMI = 0.0000 DF adjustment: Large sample DF: min = . avg = . max = . Model F test: Equal FMI F( 2, .) = 5.32 Within VCE type: Robust Prob > F = 0.0049 (Within VCE adjusted for clustering on clinic_bsl_num) ------------------------------------------------------------------------------ vf | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender | .3896755 .1532997 2.54 0.011 .0892136 .6901374 age | -.0589721 .0390038 -1.51 0.131 -.1354182 .017474 _cons | .601567 .6309789 0.95 0.340 -.6351288 1.838263 ------------------------------------------------------------------------------
Then with eform, I get the same estimates (among which a negative coefficient indicating that they have not been exponentiated):
Code:
. mi estimate: xtgee vf gender age, family(binomial) link(logit) corr(exch) vce(robust) eform Multiple-imputation estimates Imputations = 30 GEE population-averaged model Number of obs = 496 Group variable: clinic_bsl~m Number of groups = 16 Link: logit Obs per group: Family: binomial min = 10 Correlation: exchangeable avg = 31.0 Scale parameter: 1 max = 64 Average RVI = 0.0000 Largest FMI = 0.0000 DF adjustment: Large sample DF: min = . avg = . max = . Model F test: Equal FMI F( 2, .) = 5.32 Within VCE type: Robust Prob > F = 0.0049 (Within VCE adjusted for clustering on clinic_bsl_num) ------------------------------------------------------------------------------ vf | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender | .3896755 .1532997 2.54 0.011 .0892136 .6901374 age | -.0589721 .0390038 -1.51 0.131 -.1354182 .017474 _cons | .601567 .6309789 0.95 0.340 -.6351288 1.838263 ------------------------------------------------------------------------------
I have tried using the or option instead as odds ratio would be relevant as well, but that option is not allowed.
Does anyone know why it gives the same output? Is there a way to obtain exponentiated coefficient?
Thank you in advance for your help!
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