Greetings,
I'm having a problem analysing my dataset. I'm trying to get the prevalence and 95% CI using invlogit after logit, stratified by age group. The code I used for the first age group worked perfectly fine:
Code:
      . svy: logit disease i.agegroup

i.agegroup        _Iagegroup_1-4      (naturally coded; _Iagegroup_1 omitted)

Multiple-imputation estimates (svy: logit)               Imputations =      20
Survey: Logistic regression                              Minimum obs =   61763
                                                         Minimum dof =    28.1

------------------------------------------------------------------------------
 disease  |     Coef.     Std. Err.      t       P>|t|      [95% Conf. Int.]      FMI
-------------+----------------------------------------------------------------
_Iagegroup_2 |   1.17125   .662175    1.77   0.086   -.172402   2.5149       0.392
_Iagegroup_3 |   1.51965   .643212    2.36   0.025    .202209  2.83708      0.484
_Iagegroup_4 |   1.91023    .63209    3.02   0.005     .61969   3.20078       0.455
      _cons  |  -7.37062   .652405  -11.30   0.000   -8.69879  -6.04245   0.427
------------------------------------------------------------------------------

. di 100000*invlogit(_b[_cons])
62.907951

. di 100000*invlogit(_b[_cons] - invnormal(0.975)*_se[_cons])
17.521634

. di 100000*invlogit(_b[_cons] + invnormal(0.975)*_se[_cons])
225.59326
The code above resulted in the prevalence of the disease in age group 1 and its 95% CI
However, I'm stuck trying to estimate the 95% CI of other age groups. My code is like this:
Code:
. di 100000*invlogit(_b[_cons] + _b[_Iagegroup_4])
202.65838

. di 100000*invlogit(_b[_cons] + _b[_Iagegroup_4] - invnormal(0.975)*(_se[_cons] + _se[_Iagegroup_4]))
15.438546

. di 100000*invlogit(_b[_cons] + _b[_Iagegroup_4] + invnormal(0.975)*(_se[_cons] + _se[_Iagegroup_4]))
2601.186
The above 95% CI looks ridiculously wrong. Can anyone help me with the correct code to get the 95% CI for other age groups? Thank you very much!

Best regards,
Hain