calculate Median odds ratio (MOR)

[Claudia Birchenall]

Good afternoon, I have a question, I need to calculate median odds ratio in stata (MOR). I saw your post and I do it. But I am not a statistics, and I have a question. If I have to calculate the MOR in a logistic regression multilevel in the coefficients. How can I do?. I don't have a variance.

I do this: I don't Know what is the variance to calculate in the coefficients, PLEASE I NEED HELP


. melogit mortalidad edad genero1 ipmTotal ZOMAC PDET afro indigena || codi
> godivipoladepartamento: || id: , coeflegend

Fitting fixed-effects model:

Iteration 0: log likelihood = -889093.63
Iteration 1: log likelihood = -518135.36
Iteration 2: log likelihood = -503053.32
Iteration 3: log likelihood = -502096.46
Iteration 4: log likelihood = -502095.16
Iteration 5: log likelihood = -502095.16

Refining starting values:

Grid node 0: log likelihood = -498130.58

Fitting full model:

Iteration 0: log likelihood = -498130.58 (not concave)
Iteration 1: log likelihood = -498038.76 (not concave)
Iteration 2: log likelihood = -498018.82 (not concave)
Iteration 3: log likelihood = -497874.11 (not concave)
Iteration 4: log likelihood = -497813.55 (not concave)
Iteration 5: log likelihood = -497712.61
Iteration 6: log likelihood = -497653.86
Iteration 7: log likelihood = -497641.55
Iteration 8: log likelihood = -497633.73
Iteration 9: log likelihood = -497623.72
Iteration 10: log likelihood = -497618.9
Iteration 11: log likelihood = -497614.62
Iteration 12: log likelihood = -497613.67
Iteration 13: log likelihood = -497613.37
Iteration 14: log likelihood = -497613.37

Mixed-effects logistic regression Number of obs = 6313872

-------------------------------------------------------------
| No. of Observations per Group
Group Variable | Groups Minimum Average Maximum
----------------+--------------------------------------------
codigodivi~o | 36 1,953 175,385.3 1862553
id | 1,120 1 5,637.4 1862553
-------------------------------------------------------------

Integration method: mvaghermite Integration pts. = 7

Wald chi2(7) = 253629.68
Log likelihood = -497613.37 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
mortalidad | Coef. Legend
-------------+----------------------------------------------------------------
edad | .0887654 _b[edad]
genero1 | .6754843 _b[genero1]

_cons | -9.571593 _b[_cons]
-------------+----------------------------------------------------------------
codigodivi~o |
var(_cons)| .039265 _b[/var(_cons[codigodivipoladepartamento])]
-------------+----------------------------------------------------------------
codigodivi~o>|
id |
var(_cons)| .1093776 _b[/var(_cons[codigodivipoladepartamento>id])]
------------------------------------------------------------------------------
LR test vs. logistic model: chi2(2) = 8963.58 Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

.


. nlcom exp(sqrt(2*_b[edad])*invnormal(0.75)), cformat(%9.2f)

_nl_1: exp(sqrt(2*_b[edad])*invnormal(0.75))

------------------------------------------------------------------------------
mortalidad | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_nl_1 | 1.33 0.00 3499.28 0.000 1.33 1.33
------------------------------------------------------------------------------

. nlcom exp(sqrt(2*_b[genero1])*invnormal(0.75)), cformat(%9.2f)

_nl_1: exp(sqrt(2*_b[genero1])*invnormal(0.75))

------------------------------------------------------------------------------
mortalidad | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_nl_1 | 2.19 0.01 294.11 0.000 2.18 2.20
------------------------------------------------------------------------------

[Andrew Musau]

Duplicate post. If interested, see https://www.statalist.org/forums/for...atio-calculate