[R-meta] Fwd: predicted intervals in metafor

Michael Dewey li@t@ @ending from dewey@myzen@co@uk
Fri Jun 8 19:12:17 CEST 2018 

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 using gmail.com>
> Date: Fri, Jun 8, 2018 at 1:02 PM
> Subject: Re: [R-meta] predicted intervals in metafor
> To: Michael Dewey <lists using 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.
>> metareg
> 
> 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 using 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 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
>>
> 
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-- 
Michael
http://www.dewey.myzen.co.uk/home.html

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