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st: Stata's logistic vs. SAS CATMOD WLS model.


From   Ricardo Ovaldia <[email protected]>
To   [email protected]
Subject   st: Stata's logistic vs. SAS CATMOD WLS model.
Date   Mon, 20 Oct 2003 10:05:16 -0700 (PDT)

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.


=====
Ricardo Ovaldia, MS
Statistician 
Oklahoma City, OK

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