# st: Interpreting conditional logistic regression equations using 2 similar types of matching.

 From "Donald Spady" To Subject st: Interpreting conditional logistic regression equations using 2 similar types of matching. Date Tue, 6 Feb 2007 16:40:09 -0700

```Dear all
This may be a second posting.  The first one did not seem to go through and
on reading the FAQ I realized that I might have sent non-plain text. (I
copied the output from Stata using the copy table command rather than the
copy text command.  Hopefully this one will work.)

I am looking at the effect of iron-deficiency anemia on the likelihood of a
child having a seizure. I am using clogit but have a problem in that I have
matched my case/control "group" variable (age) as either very tightly
matched (same month:  "tight") or more loosely matched (up to 3 months
difference: "loose").  The whole age range of difference is 30 months, so 3
months is still fairly closely matched.  When I do the 2 clogit equations I
get different Odds ratios (which seems OK), but don't know which one is the
'right' one to use.  Any help would be appreciated.

If I use the same cases I get different odds ratios.
clogit patient Anem, group(tight) or
note: 40 groups (40 obs) dropped due to all positive or
all negative outcomes.

Iteration 0:   log likelihood = -197.63842
Iteration 1:   log likelihood = -197.44509
Iteration 2:   log likelihood = -197.44481
Iteration 3:   log likelihood = -197.44481

Conditional (fixed-effects) logistic regression   Number of obs   =
574
LR chi2(1)      =
2.98
Prob > chi2     =
0.0845
Log likelihood = -197.44481                       Pseudo R2       =
0.0075

----------------------------------------------------------------------------
--
patient | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
Anem |   2.142857   .9808665     1.67   0.096     .8737077
5.255575
----------------------------------------------------------------------------
--
r; t=0.22 16:31:34

. clogit patient Anem, group(loose) or
note: 112 groups (112 obs) dropped due to all positive or
all negative outcomes.

Iteration 0:   log likelihood = -173.23535
Iteration 1:   log likelihood = -173.23185
Iteration 2:   log likelihood = -173.23185

Conditional (fixed-effects) logistic regression   Number of obs   =
502
LR chi2(1)      =
1.50
Prob > chi2     =
0.2213
Log likelihood = -173.23185                       Pseudo R2       =
0.0043

----------------------------------------------------------------------------
--
patient | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
Anem |   1.538462   .5480964     1.21   0.227     .7653064
3.092701
----------------------------------------------------------------------------
--
r; t=0.16 16:31:43

In both cases the cases used are the same; the cases and controls are paired
on age but using slightly different criteria.  One equation drops 40 groups
and the other drops 112 groups.  The total number of groups is the same for
both equations.
My questions are:
1. Is it reasonable to see such a 'big' difference in OR between the two
equations?
2. Which is more likely to give the most reliable OR; the equation where the
matching was rigid, or the equation where the matching is more relaxed?

Many thanks

Departments of Pediatrics and Public Health Sciences
8226B, Aberhart Centre 1
University of Alberta
Edmonton, Alberta,
T6G 2J3

780-407-1244: Office
780-407-7136: FAX

Nature has no reset button!

*
*   For searches and help try:
*   http://www.stata.com/support/faqs/res/findit.html
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/
```