[R-meta] predicted intervals in metafor

[Michael Dewey]

Dear Ray

Not sure whether you have seen this page?

http://www.metafor-project.org/doku.php/analyses

You need examples for mixed effects but all the examples are worth 
examining as are the other pages found via the left navigation.

Michael

On 07/06/2018 18:41, Raynaud Armstrong wrote:
> 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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-- 
Michael
http://www.dewey.myzen.co.uk/home.html