Thank you very much Paul. It seems to me that this
model is telling me the same as my original model but
in a more convoluted way. In my original logistic
model I included the before measurement as a RHS
variable and the after measurement as the outcome. In
that model -intervention- was significant which I
interpreted as the intervention has an effect on the
outcome. In this new model -intervention- is not
significant, however the -intervention*time- is, but
the conclusion is the same. Is this correct, or am I
missing something?
I really appreciate your time and your help.
Best,
Ricardo.
--- VISINTAINER PAUL <[email protected]>
wrote:
> You can interpret it by looking at your outcome over
> time, within each level
> of your intervention. By inputting your equation, I
> get:
>
>
> +----------------------------+
> | time intern it y |
> |----------------------------|
> 1. | 0 0 0 -.08 |
> 2. | 1 0 0 0 |
> 3. | 0 1 0 -.447 |
> 4. | 1 1 1 .708 |
> +----------------------------+
>
> So, in the placebo group, Y changes from -.08 to 0,
> over time. In the
> intervention group, Y changes from -.447 to .708,
> over time. In other
> words, the placebo group has a near 0 slope over
> time, while the
> intervention has a significantly positive slope over
> time. That's what the
> significant interaction term is telling you.
>
> Paul
>
> P.S. If you plot these, remember that these are
> linear logits, if you
> exponentiate them, values are no longer linear.
>
>
>
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On
> Behalf Of Ricardo Ovaldia
> Sent: Thursday, October 23, 2003 12:10 PM
> To: [email protected]
> Subject: Re: st: RE: Stata's logistic vs. SAS CATMOD
> WLS model.
>
> 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|
>
=== message truncated ===
=====
Ricardo Ovaldia, MS
Statistician
Oklahoma City, OK
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