Thank you Joseph and Kieran.
I originally though to model this problem as Joseph's
"ANCOVA-like approach" but without the interaction
term (i.e.):
xi: logistic followup i.baseline i.intervention
If I do these, isn't the test: Beta(intervention)=0
testing whether the intervention had an effect? I am
not certain what the interaction term adds in this
context? Please excuse me if this is a stupid
question, but I do not get it. What am I missing?
Thank you,
Ricardo.
--- Joseph Coveney <[email protected]> wrote:
>
> Ricardo Ovaldia asked about a pre-post randomized
> parallel-group trial
> design with a binary outcome variable and Kieran
> McCaul suggested a
> repeated-measures (conditional logistic regression)
> approach.
>
> I have three follow-up questions:
>
> 1. Which is better for binary outcomes, Kieran's
> repeated-measures approach
> or an ANCOVA-like approach using the pretreatment
> values as a baseline
> covariate in conventional logistic regression? The
> do-file below suggests
> that completely different conclusions would be drawn
> from the same dataset
> depending upon which approach is used to analyze it.
>
> 2. As Kieran mentioned, the repeated-measures
> approach drops one of the
> "main effects" (treatment) so that the model ends up
> having an interaction
> term in it when one of the component "main effects"
> terms contributing to
> the interaction is not in the model. This would be
> a no-no from what I've
> heard, at least for the analogous situation in
> ANOVA. But, I assume that
> this *not* a problem for conditional logistic
> regression due to the
> conditioning. Is that correct?
>
> 2. When using the likelihood-ratio test (-lrtest-),
> which is the proper
> model against which to compare for testing
> individual "main effects" of
> treatment and baseline--the saturated model (*with*
> the interaction) or the
> partially reduced model (*no* interaction term,
> i.e., the model that
> includes only both of the main effects)? Or should
> we be testing a
> constant-only model against one with the "main
> effect" in order to test that
> "main effect"?
>
> Joseph Coveney
>
>
----------------------------------------------------------------------------
>
> clear
> set more off
> set seed 20040129
> * Fictional dataset based upon Ricardo's description
> set obs 328
> generate byte intervention = _n > _N / 2
> generate byte baseline = 0
> display 328 / 2 * 0.29
> replace baseline = 1 in 1/66
> display 328 / 2 * 0.40
> replace baseline = 1 in 165/212
> tabulate intervention baseline, row
> generate float ord = uniform() // no particular
> correlation
> sort intervention ord
> drop ord
> generate byte followup = 0
> replace followup = 1 in 1/85
> replace followup = 1 in 165/259
> tabulate intervention followup, row
> * ANCOVA-like approach
> xi: logistic followup i.baseline*i.intervention
> estimates store A // Saturated model
> xi: logistic followup i.baseline i.intervention
> estimates store B // "main effects" only model
> lrtest A B, stats
> xi: logistic followup i.baseline
> lrtest A ., stats // This one?
> lrtest B ., stats // Or this one?
> xi: logistic followup i.intervention
> lrtest A ., stats // This one?
> lrtest B ., stats // Or this one?
> * Repeated-measures approach
> generate int personid = _n
> rename baseline seatbelt0
> rename followup seatbelt1
> reshape long seatbelt, i(personid) j(period)
> xi: clogit seatbelt i.intervention*i.period,
> group(personid)
> estimates store C
> xi: clogit seatbelt i.period, group(personid)
> lrtest C ., stats
> exit
>
>
>
>
> *
> * 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/
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