Thank you Paul. Following your advised I get:
. logit meter time intervention it, cluster(id)
Iteration 0: log pseudo-likelihood = -277.17887
Iteration 1: log pseudo-likelihood = -268.85134
Iteration 2: log pseudo-likelihood = -268.84209
Iteration 3: log pseudo-likelihood = -268.84209
Logit estimates
Number of obs = 400
Wald
chi2(3) = 23.65
Prob
> chi2 = 0.0000
Log pseudo-likelihood = -268.84209
Pseudo R2 = 0.0301
(standard errors
adjusted for clustering on id)
------------------------------------------------------------------------------
| Robust
meter | Coef. Std. Err. z P>|z|
[95% Conf. Interval]
-------------+----------------------------------------------------------------
time | .0800427 .1964444 0.41 0.684
-.3049812 .4650666
intervention | -.3672695 .2872475 -1.28 0.201
-.9302643 .1957252
it | 1.075455 .3101891 3.47 0.001
.4674952 1.683414
_cons | -.0800427 .2006625 -0.40 0.690
-.4733339 .3132485
------------------------------------------------------------------------------
Which is not exactly what SAS produces, but like SAS,
it gives a significant interaction and a
non-significant intervention effect. How do I
interpret the interaction in this context?
Thank you again,
Ricardo.
--- VISINTAINER PAUL <[email protected]>
wrote:
> 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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=====
Ricardo Ovaldia, MS
Statistician
Oklahoma City, OK
__________________________________
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