# st: RE: Stratify analysis - logistic regression with dummies

 From "Visintainer, Paul" To Subject st: RE: Stratify analysis - logistic regression with dummies Date Thu, 5 Jun 2008 10:49:26 -0400

```Ricardo,

The difference is probably due to the fact that you are developing your
models on different samples sizes, and as a consequence, a different
mean age for each sample.  This isn't a problem when you are computing
-logit- command with the equivalent -tabodds- command.  In your example,
your first logit command -xi: logistic low i.race- is computing the ORs
for a 3x2 table.  You can replicate the logit command by running
-tabodds low race, or-).

When you add age as a continuous variable to your model AND use the "if"
statement, your model is alternatively excluding observations who are
either RACE2 or RACE3, thus your sample size changes (e.g, n=122 or
n=163).  The adjustment for age is based on the mean age for sample
being used to estimate the OR.  Thus, as you change the samples change
so does the mean age:

For n=189: mean age== 23.2381
For n=163: mean age== 23.5092  (no RACE2)
For n=122: mean age== 23.7049 (no RACE3)

-p

______________________________________
Paul F. Visintainer, PhD
Department of Epidemiology and Biostatistics
School of Public Health
New York Medical College
PH: (914) 594-4804
FX: (914) 594-4853

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Ricardo
Ovaldia
Sent: Wednesday, June 04, 2008 10:15 AM
To: statalist@hsphsun2.harvard.edu
Subject: st: Stratify analysis - logistic regression with dummies

I am confused by some of the result that I got. I will illustrate using
Hosmer & Lemeshow' low weight data:

. use http://www.stata-press.com/data/r10/lbw.dta
(Hosmer & Lemeshow data)

if I fit

. xi:logistic low i.race

and then fit

. xi:logistic low i.race if race==1 | race==2

and

. xi:logistic low i.race if race==1 | race==3

I get the same OR for  _Irace_2  and _Irace_3 as I do for the full
model. This is as expected because the dummies are ortogonal to each
other.

However, when a covariate is added to the model, the same is not true
anymore:

. xi:logistic low i.race age

low | Odds Ratio   Std. Err.      z    P>|z|  [95% Conf.
Interval]
-------------+----------------------------------------------------------
---
_Irace_2 |   2.106974   .9932407     1.58   0.114   .8363679
5.307878
_Irace_3 |   1.767748   .6229325     1.62   0.106   .8860686
3.526738
age |   .9612592   .0311206    -1.22   0.222   .9021588
1.024231
------------------------------------------------------------------------
---

. xi:logistic low i.race age if race==1 | race==2

------------------------------------------------------------------------
---
low | Odds Ratio   Std. Err.      z    P>|z|  [95% Conf.
Interval]
-------------+----------------------------------------------------------
---
_Irace_2 |   2.155207   1.021287     1.62   0.105    .8513944
5.45566
age |   .9705512   .0376446    -0.77   0.441    .8995039
1.04721
------------------------------------------------------------------------
---

. xi:logistic low i.race age if race==1 | race==3

------------------------------------------------------------------------
---
low | Odds Ratio   Std. Err.      z    P>|z|  [95% Conf.
Interval]
-------------+----------------------------------------------------------
---
_Irace_3 |   1.724551   .6098827     1.54   0.123    .8622856
3.449063
age |   .9440875   .0340586    -1.59   0.111    .8796392
1.013258
------------------------------------------------------------------------
---

There is no missing data.

I am very confused about which OR to reports and what are the
differences in these models. I was not expecting these results.

Ricardo.

Ricardo Ovaldia, MS
Statistician
Oklahoma City, OK

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```