I have a similar question to one posed a few years ago in this thread. I am running an adjusted poisson model with a binary outcome and a binary-by-binary interaction term (the log binomial model wouldn't converge). The outcome is medication adherence and the interaction is between a dichotomous measure of stigma and a dichotomous measure of medication norms. The output from the model shows a significant multiplicative interaction. To present the interaction, I am trying to obtain the effect of stigma on the relative risk of medication adherence within the "strata" of the medication norms variable; or, what is the effect of stigma given the presence of medication norms, and what is the effect of stigma given the absence of adherence norms. I used nlcom to obtain these RRs, although in the thread cited above, Martin Buis raises concerns with this approach. Below is the output. See the bold for the results I am comparing. I would like to be able to show that the confidence intervals for the RRs given the presence of norms and the absence of norms do not overlap (and thus, there is a significant multiplicative interaction) but the results from nlcom seem a little off. The RR of the interaction from the model and from nlcom are similar but the confidence intervals and p value are different. Is there a different way to do this?
Many thanks,
Robbie
Code:
. poisson medbi i.hidebi3##i.adnorm2 i.yrcat2 depr23 crack fdhivbi famhivbi pknowbi, irr vce(robust)
Iteration 0: log pseudolikelihood = -192.43444
Iteration 1: log pseudolikelihood = -192.42429
Iteration 2: log pseudolikelihood = -192.42428
Poisson regression Number of obs = 221
Wald chi2(10) = 29.94
Prob > chi2 = 0.0009
Log pseudolikelihood = -192.42428 Pseudo R2 = 0.0355
-------------------------------------------------------------------------------------
| Robust
medbi | IRR Std. Err. z P>|z| [95% Conf. Interval]
--------------------+----------------------------------------------------------------
hidebi3 |
Very much | .3475176 .1568795 -2.34 0.019 .1434564 .8418482
|
adnorm2 |
Adherent | 1.29926 .1346596 2.53 0.012 1.060412 1.591906
|
hidebi3#adnorm2 |
Very much#Adherent | 3.161404 1.635853 2.22 0.026 1.146648 8.716255
|
yrcat2 |
10-19 yrs | 1.037215 .1438793 0.26 0.792 .7903014 1.361272
20+ years | .960983 .1323098 -0.29 0.773 .7337039 1.258666
|
depr23 | .791829 .1188083 -1.56 0.120 .5900851 1.062547
crack | .7794588 .0881733 -2.20 0.028 .6244595 .972931
fdhivbi | .8665909 .1037859 -1.20 0.232 .685285 1.095865
famhivbi | 1.09182 .1187786 0.81 0.419 .8821638 1.351304
pknowbi | .87816 .0974026 -1.17 0.241 .7065797 1.091405
_cons | .7569053 .1165931 -1.81 0.071 .5596585 1.02367
-------------------------------------------------------------------------------------
Note: _cons estimates baseline incidence rate.
. margins, at(adnorm2=(0 1) hidebi3=(0 1)) predict(pr()) post
Predictive margins Number of obs = 221
Model VCE : Robust
Expression : Predicted number of events, predict(pr())
1._at : hidebi3 = 0
adnorm2 = 0
2._at : hidebi3 = 0
adnorm2 = 1
3._at : hidebi3 = 1
adnorm2 = 0
4._at : hidebi3 = 1
adnorm2 = 1
------------------------------------------------------------------------------
| Delta-method
| Margin Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_at |
1 | .5785924 .0410927 14.08 0.000 .4980522 .6591326
2 | .7517419 .0563219 13.35 0.000 .641353 .8621309
3 | .201071 .0894456 2.25 0.025 .0257609 .3763811
4 | .8258964 .1967121 4.20 0.000 .4403478 1.211445
------------------------------------------------------------------------------
. est sto m4
.
. *RRs stratified by norms
. *RR for stigma given no norms
. nlcom _b[3._at]/_b[1._at]
_nl_1: _b[3._at]/_b[1._at]
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_nl_1 | .3475176 .1568795 2.22 0.027 .0400394 .6549958
------------------------------------------------------------------------------
.
. *RR for stigma given norms
. nlcom _b[4._at]/_b[2._at]
_nl_1: _b[4._at]/_b[2._at]
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_nl_1 | 1.098644 .2726728 4.03 0.000 .5642146 1.633072
------------------------------------------------------------------------------
.
. *Ratio of RRs
. nlcom (_b[4._at]/_b[2._at])/(_b[3._at]/_b[1._at])
_nl_1: (_b[4._at]/_b[2._at])/(_b[3._at]/_b[1._at])
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_nl_1 | 3.161404 1.635853 1.93 0.053 -.044808 6.367616
------------------------------------------------------------------------------
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