[R-meta] Subgroup analysis output using metafor - interpretation

[Joao Afonso]

Dear Michael,

Thank you for your prompt reply. Sorry for my eventual mistake with
the *metaprop
*command. It is likely that it comes from the *meta* package. I had to
install it as well as the *metafor* package, to run model.

I have log-transformed the number of *events *and *n *using logit
transformed proportion:

*ies.logit=escalc(xi=nlameanimal, ni=ssizeanimal,
data=prevalence_2020_nomv, measure="PLO")*
*pes.logit=rma(yi, vi, data=ies.logit, method = "DL")*
*pes=predict(pes.logit, transf=transf.ilogit)*
*print(pes.logit, digits=4)*

As for the outliers I could take a step back and instead of removing them
leave them in the data-set and see what happens when conducting the
sub-group analysis. Is this best practice when conducing a meta-analysis?

Outputs below


   - Output from meta-analysis after removing outliers


* Random-Effects Model (k = 42; tau^2 estimator: DL)*



* tau^2 (estimated amount of total heterogeneity): 0.2307 (SE = 0.1768)*

* tau (square root of estimated tau^2 value): 0.4803*

* I^2 (total heterogeneity / total variability): 99.73%*

* H^2 (total variability / sampling variability): 372.29*



* Test for Heterogeneity:*

* Q(df = 41) = 15263.8748, p-val < .0001*



* Model Results:*



* estimate se zval pval ci.lb <http://ci.lb> ci.ub*

* -0.8445 0.0802 -10.5351 <.0001 -1.0016 -0.6874 ****



* ---*

* Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1*

*confint(pes.logit, digits=2)*



* estimate ci.lb <http://ci.lb> ci.ub*

* tau^2 0.23 0.60 1.68*

* tau 0.48 0.77 1.30*

* I^2(%) 99.73 99.90 99.96*

* H^2 372.29 963.40 2707.90*

*print(pes, digits=4)*



* pred ci.lb <http://ci.lb> ci.ub cr.lb <http://cr.lb> cr.ub*

* 0.3006 0.2686 0.3346 0.1420 0.5274*




   - Output from subgroup analysis with lcmbi as moderator:


 *Mixed-Effects Model (k = 42; tau^2 estimator: DL)*



* tau^2 (estimated amount of residual heterogeneity): 0.0977 (SE = 0.0677)*

* tau (square root of estimated tau^2 value): 0.3125*

* I^2 (residual heterogeneity / unaccounted variability): 99.17%*

* H^2 (unaccounted variability / sampling variability): 120.25*

* R^2 (amount of heterogeneity accounted for): 57.67%*



* Test for Residual Heterogeneity:*

* QE(df = 40) = 4809.8552, p-val < .0001*



* Test of Moderators (coefficient 2):*

* QM(df = 1) = 1.5762, p-val = 0.2093*



* Model Results:*



* estimate se zval pval ci.lb <http://ci.lb> ci.ub*

* intrcpt -0.8191 0.0625 -13.1124 <.0001 -0.9416 -0.6967 ****

* lcmbiRecords -0.1653 0.1317 -1.2555 0.2093 -0.4234 0.0928*



* ---*

* Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1*

*print(pes.subg.lcmbi[1], digits=6)*



* pred ci.lb <http://ci.lb> ci.ub cr.lb <http://cr.lb> cr.ub*

* 1 0.305946 0.280581 0.332544 0.190968 0.451515*

*print(pes.subg.lcmbi[17], digits=6)*



* pred ci.lb <http://ci.lb> ci.ub cr.lb <http://cr.lb> cr.ub*

* 17 0.305946 0.280581 0.332544 0.190968 0.451515*

*print(pes.lcmbi, digits=6)*



* pred ci.lb <http://ci.lb> ci.ub cr.lb <http://cr.lb> cr.ub*

* 0.294637 0.264094 0.327143 0.253791 0.339071*





Many thanks for all the help Michael



On Thu, Jan 9, 2020 at 1:57 PM Michael Dewey <lists using dewey.myzen.co.uk>
wrote:

> Dear Joao
>
> I hink we may need some clarification before we can answer this.
> Comments in-line below
>
> On 09/01/2020 13:36, Joao Afonso wrote:
> > Dear all,
> >
> > I am running a meta-analysis on the prevalence of lameness (binary) in
> > British dairy cattle and have used the *metaprop* from the *metafor*
> package.
>
> I think metaprop comes from meta, not metafor?
>
> > I have set the model to run with random effects, using the DL method, and
> > have taken the following approach:
> >
> >     1. log-transform the data as it is not normally distributed
>
> If it is binary data I would not have expected that anyway so what
> exactly did you transform?
>
> >     2. identify outliers using influential analysis (only ran this once)
> >     3. remove outliers and rerun the model
>
> In general that seems a bad idea as it removes the most interesting
> observations but you may have reasons to doubt the observations.
>
> >     4. deal with remaining heterogeneity with subgroup analysis and
> >     meta-regression
> >
> > I have ran the model and am getting what I believe conflicting evidences
> on
> > different output indicators. As an example, after running subgroup
> analysis
> > with one moderator, the output tells me that the moderator explains
> around
> > 50% of the heterogeneity (R^2), and yet the p-value for the test of
> > moderators is substantially higher than 0.05 telling me that the pooled
> > estimates of the subgroups aren't actually different.
>
> Can you share the output from the analysis to give us a clue?
>
>
> >
> >
> >
> > I was hoping you could shed a light as to what could justify this
> happening
> > (if it makes sense), and possibly provide some guidance as to what I
> could
> > do to improve the statistical evidences of my study.
> >
> >
> > Many thanks and happy 2020 to everyone
> >
>
> --
> Michael
> http://www.dewey.myzen.co.uk/home.html
>


-- 
João Afonso
*DVM, MSc Veterinary Epidemiology*
*PhD Student *
*Department of Infection and Global Health*
*University of Liverpool*
*+351914812305*

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