[R-meta] Meta-analyzing ORs from GEEs
Michael Dewey
li@t@ @ending from dewey@myzen@co@uk
Wed Aug 29 09:49:52 CEST 2018
Inline
On 29/08/2018 03:13, Mark White wrote:
> Let's say I have 30 studies, where each study measures a dichotomous
> outcome and has a dichotomous predictor. Some people have multiple
> responses, so I use a GEE to model every one of the 30 studies. What I have
> is a log-odds coefficient for each (via `coef(model)[2]` for each study),
> as well as the variance for each of these coefficients (via
> `diag(vcov(model))[2]` for each study). Note that no covariates are added
> to the model. The only coefficients are the intercept and the one for the
> dichotomous predictor.
>
> Two questions for everyone:
>
> 1. Can I assign the coefficients (in logits—the log of the odds ratio) to
> `yi` and the variances to `vi` from these GEE models and submit them
> directly to `rma.uni()`? I am assuming here that the coefficients and
> variances are all I need, and the `escalc` function says we should be
> converting odds ratios to log-odds anyways. Note that there are no
> dependencies *across *studies, so the robust variance estimates from the
> GEE should capture all the dependencies.
>
That seems OK to me since you state there are no other covariates to
worry about.
> 2. If I can do that, what is the recommended way to present this to
> non-statistical audiences? I can get a meta-analytic estimate for the log
> of the odds ratio, but my clients are used to seeing risk differences
> (e.g., "There was a +4 percentage point lift"). I could always convert
> log-odds to odds ratios and then compute lifts from a variety of different
> baselines (e.g., "If there was a 50% positive outcome in the control, then
> we had a +3.5 percentage point lift"). Any other ideas?
>
That seems a good idea. Never heard lift used in that sense but if that
is their preferred term it is perfectly clear.
Michael
> Thanks,
> Mark
>
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>
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Michael
http://www.dewey.myzen.co.uk/home.html
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