Ray, please keep the list in the loop and please register so we do not
have to approve each post.
Michael
On 08/06/2018 18:03, Raynaud Armstrong wrote:
---------- Forwarded message ----------
From: Raynaud Armstrong <raynaud.armstrong at gmail.com>
Date: Fri, Jun 8, 2018 at 1:02 PM
Subject: Re: [R-meta] predicted intervals in metafor
To: Michael Dewey <lists at dewey.myzen.co.uk>
Thanks Michael. *I got the following output for my meta-regression. My
heterogeneity statistics are all 0 - what does this mean?*
#meta-regression
metareg <- rma (yi=cohen, vi=se_cohen, mods = ~ mean_age + year +
country, data=metav)
Warning message:
In rma(yi = cohen, vi = se_cohen, mods = ~mean_age + year + country, :
Studies with NAs omitted from model fitting.
Mixed-Effects Model (k = 7; tau^2 estimator: REML)
tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.1825)
tau (square root of estimated tau^2 value): 0
I^2 (residual heterogeneity / unaccounted variability): 0.00%
H^2 (unaccounted variability / sampling variability): 1.00
R^2 (amount of heterogeneity accounted for): NA%
Test for Residual Heterogeneity:
QE(df = 3) = 0.3265, p-val = 0.9550
Test of Moderators (coefficient(s) 2:4):
QM(df = 3) = 1.6838, p-val = 0.6406
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt 39.8696 65.5910 0.6079 0.5433 -88.6863 168.4255
mean_age 0.0456 0.0373 1.2223 0.2216 -0.0275 0.1187
year -0.0203 0.0332 -0.6109 0.5413 -0.0852 0.0447
countryUSA -0.1974 0.3959 -0.4987 0.6180 -0.9733 0.5785
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Please let me know. Thanks.
Ray
On Fri, Jun 8, 2018 at 7:02 AM, Michael Dewey <lists at dewey.myzen.co.uk>
wrote:
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 at 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 at gmail.com]
Sent: Tuesday, 05 June, 2018 19:24
To: r-sig-meta-analysis at 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 at 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 at gmail.com]
Sent: Tuesday, 05 June, 2018 17:57
To: r-sig-meta-analysis at 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 at 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 at 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 at r-project.org] On Behalf Of Raynaud Armstrong
Sent: Friday, 15 December, 2017 1:37
To: r-sig-meta-analysis at 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
[[alternative HTML version deleted]]