[R-meta] Fwd: predicted intervals in metafor

Raynaud Armstrong r@yn@ud@@rm@trong @ending from gm@il@com
Fri Jun 8 19:24:24 CEST 2018 

Hi Michael,
Sorry, I had forgotten to reply to the subscription email. I believe I am
now registered.
My apologies once again.

Ray

On Fri, Jun 8, 2018 at 1:12 PM, Michael Dewey <lists using dewey.myzen.co.uk>
wrote:

> 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
>>>>>
>>>>>
>>>>>          [[alternative HTML version deleted]]
>>>>
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>>>>
>>>>
>>>> --
>>> Michael
>>> http://www.dewey.myzen.co.uk/home.html
>>>
>>>
>>         [[alternative HTML version deleted]]
>>
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>>
> --
> Michael
> http://www.dewey.myzen.co.uk/home.html
>

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