I haven't' tried this, but I think it will work.
Set up your data as:
Meter usage: 0 - no, 1 - yes
Time: 0 pre, 1 is post
Intervention: 0 - no; 1 yes
meter time intervn id
1. 0 1 1 1
2. 0 0 0 1
3. 0 1 0 2
4. 1 0 0 2
. . . etc.
Then, use either xtlogit or logit with cluster(id). You can generate an
interaction term between intervention and time. Something like:
.gen it = intern*time
.logit meter time intervention it, cluster(id)
Paul Visintainer
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Ricardo Ovaldia
Sent: Thursday, October 23, 2003 9:29 AM
To: [email protected]
Subject: st: Stata's logistic vs. SAS CATMOD WLS model.
Dear all,
Last week I posted a question and did not received any
replies. I would I appreciate any comments regarding
the logistic model that I used. Is there a better way
to do this in Stata. I rather not have to use SAS.
Thank you in advance. Ricardo
In an intervention study geared to teach diabetics
about glucose monitoring, 100 patients were randomized
to receive a standard educational method, and 100
patients to receive a new method. One of the outcomes
of interest is whether or not the patient could use
the glucose-meter correctly or not, as determined by
comparing their reported values with those obtained by
a trained laboratory tech.
Each patient was tested twice; before the intervention
and two weeks after the intervention. Here is some of
the data excluding covariates.
. cl
interve~n before after
1. 0 1 1
2. 0 0 0
3. 0 1 0
4. 1 0 0
5. 1 1 1
6. 1 0 1
I analyzed this data using -logistic- by including
-before- as a RHS variable:
. logistic after before intervention
Logistic regression
Number of obs = 200
LR
chi2(2) = 46.68
Prob
> chi2 = 0.0000
Log likelihood = -112.38704
Pseudo R2 = 0.1720
----------------------------------------------------------------------------
--
after | Odds Ratio Std. Err. z P>|z|
[95% Conf. Interval]
-------------+--------------------------------------------------------------
--
before | 8.061982 2.90929 5.78 0.000
3.974411 16.35351
intervention | 2.971752 1.009034 3.21 0.001
1.527546 5.781374
----------------------------------------------------------------------------
--
which indicates to me that the new method is superior
to the standard method. When I presented the results
one of the researchers suggested I use SAS's CATMOD
Weighted Least Squares procedure to analyze these
data. Following an example in the SAS manual I
obtained:
Analysis of Weighted Least Squares
Estimates
Standard
Chi-
Effect Parameter Estimate Error
Square Pr > ChiSq
Intercept 1 0.5100 0.0293
302.44 <.0001
intervention 2 -0.0200 0.0293
0.47 0.4952
time 3 -0.0750 0.0184
16.63 <.0001
intervention*time 4 0.0650 0.0184
12.49 0.0004
Now, the time-by-intervention is significant but not
the intervention term. Not being a SAS user, or
familiar with CATMOD, I am not sure whether or not
these results contradict my prior analysis. Is there
any way to do what SAS is doing using STATA? Any help
would be greatly appreciated. Here is the SAS code I
used:
proc catmod order=data;
response marginals;
model before*after=intervention| _response_;
repeated time;
Thank you,
Ricardo.
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