# [R-meta] Fwd: predicted intervals in metafor

Raynaud Armstrong r@yn@ud@@rm@trong @ending from gm@il@com
Tue Jun 5 19:23:39 CEST 2018

```---------- Forwarded message ----------
From: Raynaud Armstrong <raynaud.armstrong using gmail.com>
Date: Tue, Jun 5, 2018 at 1:23 PM
Subject: Re: [R-meta] predicted intervals in metafor
To: "Viechtbauer, Wolfgang (SP)" <
wolfgang.viechtbauer using maastrichtuniversity.nl>

Dear Wolfgang,
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?

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?
> 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 using maastric
> htuniversity.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)
>
> 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-bo
> unces 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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