[R-meta] predicted intervals in metafor

[Raynaud Armstrong]

Dear Wolfgang,
Thanks for your reply. It was indeed helpful.
Are there any good meta-regression tutorials or examples in R using metafor
package?

Please let me know.
Thanks
Ray

On Wed, Jun 6, 2018 at 5:02 AM, Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> Dear Rav,
>
> The default is REML.
>
> Checking whether the sampling distribution of the outcome measure is
> normal is not something that you can do in your observed data. Outcome
> measures typically used in meta-analysis all have, at least asymptotically,
> a normal sampling distribution. So, as long as your sample sizes are not
> too small, this is not something you typically have to worry about.
>
> As for the distribution of the true effects: In principle, this can be
> checked in the actual data, but this is difficult to do. One possible
> approach is to examine the distribution of the predicted random effects,
> which you can get with the ranef() function.
>
> Best,
> Wolfgang
>
> -----Original Message-----
> From: Raynaud Armstrong [mailto:raynaud.armstrong using gmail.com]
> Sent: Tuesday, 05 June, 2018 19:24
> To: r-sig-meta-analysis using r-project.org; Viechtbauer, Wolfgang (SP)
> Subject: Fwd: [R-meta] predicted intervals in metafor
>
> Dear Wolfgang,
> Thanks for your reply.
> Sorry for not being clear. I was just wanting to know if I needed to check
> normality of my effect sizes collectively - which you have already
> answered. I would also like to know the default method in random effects
> meta-analysis in metafor - is it REML or DL?
>  How can we test the assumption that the sampling distributions are normal
> and that the underlying true effects are normal?
>
> Looking forward to your reply,
>
> Thanks
> RAv
>
> On Tue, Jun 5, 2018 at 12:48 PM, Viechtbauer, Wolfgang (SP) <
> wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> Dear Raynaud,
>
> Not sure what you mean by 'default method'. Do you mean the method for
> computing the prediction interval? By default, it is:
>
> mu-hat +/- 1.96 sqrt(SE(mu-hat)^2 + tau^2)
>
> where 'mu-hat' is the estimate of mu, SE(mu-hat) is the corresponding
> standard error, and tau^2 is the estimated amount of heterogeneity
> (between-study variance).
>
> When using the Knapp & Hartung method (argument: test="knha"), then
> instead of 1.96 (or rather qnorm(.975) to be exact), the equation uses the
> 97.5th percentile from a t-distribution with k-1 df (where k is the number
> of studies).
>
> As for the distribution of the effect sizes: The RE model does not assume
> that the collection of observed effects is normally distributed. It assumes
> that the sampling distributions are normal and that the underlying true
> effects are normal. However, that does not imply that the (marginal)
> distribution of the observed effects is normally distributed.
>
> Best,
> Wolfgang
>
> -----Original Message-----
> From: Raynaud Armstrong [mailto:raynaud.armstrong using gmail.com]
> Sent: Tuesday, 05 June, 2018 17:57
> To: r-sig-meta-analysis using r-project.org; Viechtbauer, Wolfgang (SP)
> Subject: Fwd: [R-meta] predicted intervals in metafor
>
> Hi everyone!
> I would like to know what is the default method in random effects
> meta-analysis in metafor.
> My effect size (cohen's d) is not normally distributed. Does that matter?
> Please reply as soon as possible.
> Thanks
> RAv
> ---------- Forwarded message ----------
> From: Raynaud Armstrong <raynaud.armstrong using gmail.com>
> Date: Sat, Dec 16, 2017 at 6:34 AM
> Subject: Re: [R-meta] predicted intervals in metafor
> To: "Viechtbauer Wolfgang (SP)" <wolfgang.viechtbauer@
> maastrichtuniversity.nl>
>
> Perfect - it worked!
> Thanks
>
> On Fri, Dec 15, 2017 at 3:38 AM, Viechtbauer Wolfgang (SP) <
> wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> Dear Raynaud,
>
> Once you have the SMD values and corresponding sampling variances, the
> code is the same. Here is an example:
>
> library(metafor)
>
> ### load data
> dat <- get(data(dat.normand1999))
>
> ### calculate SMDs and corresponding sampling variances
> dat <- escalc(measure="SMD", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i,
> sd2i=sd2i, n2i=n2i, data=dat)
> dat
>
> ### meta-analysis of SMD values using a random-effects model
> res <- rma(yi, vi, data=dat)
> res
>
> ### get prediction/credibility interval
> predict(res)
>
> If you have calculated the SMD values and variances yourself, you can skip
> the escalc() step and go straight to rma(). Adjust variables names as
> needed.
>
> Best,
> Wolfgang
>
> -----Original Message-----
> From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-
> bounces using r-project.org] On Behalf Of Raynaud Armstrong
> Sent: Friday, 15 December, 2017 1:37
> To: r-sig-meta-analysis using r-project.org
> Subject: [R-meta] predicted intervals in metafor
>
> Hi there,
> I would like to calculate predicted intervals in addition to my pooled
> estimate and CIs as I have plenty of between-study variation in my
> meta-analysis. I am using metafor package and my summary estimated is an
> effect size (SMD) and not odds ratios. The examples I have come across
> mainly focus on odds ratios and I wonder what to do for SMDs.
> I would appreciate if someone could suggest what function to use.
>
> Thank you,
> Raynaud
>

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