[R-meta] variance of predicted effect sizes

Crean, Hugh hugh_cre@n @ending from URMC@Roche@ter@edu
Mon Jan 7 23:18:34 CET 2019

Hi Wolfgang et al.,

Yes, it is the working out the correct equations that have myself and colleagues stumped right now.

Thanks again,


-----Original Message-----
From: Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl> 
Sent: Monday, January 07, 2019 10:52 AM
To: Crean, Hugh <hugh_crean using URMC.Rochester.edu>; 'r-sig-meta-analysis using r-project.org' <r-sig-meta-analysis using r-project.org>
Subject: RE: variance of predicted effect sizes

Hi Hugh,

"Is there a way of incorporating both sets of dependencies into the V matrix?" Probably, but one would have to work out the correct equations for the covariances (I am not aware of anybody who has done this already).


>-----Original Message-----
>From: Crean, Hugh [mailto:hugh_crean using URMC.Rochester.edu]
>Sent: Wednesday, 02 January, 2019 22:39
>To: Viechtbauer, Wolfgang (SP); 'r-sig-meta-analysis using r-project.org'
>Subject: RE: variance of predicted effect sizes
>Hello Wolfgang et al.,
>Yes to the below and thanks again so much.  To start, I have been 
>trying to implement the simpler approach of just using the weighted 
>average control group effects for those studies not having a control 
>group and as you mention below, using the squared SE as the sampling 
>variance.  I have an additional wrinkle, however, and I cannot find 
>much guidance in the literature.  A few of the studies have their own 
>dependencies (multiple treatment arms in the same study either with or without a control group).
>Is there a way of incorporating both sets of dependencies into the V 
>matrix? For now, I am thinking along the lines of using a sensitivity 
>analyses where two estimates are computed -- the first would just add 
>the two estimates together as this would provide a high estimate of the 
>possible covariance and the second would just take the higher of the 
>two covariances.  Neither feels satisfying (aside from difficulties 
>with non- positive matrices) but does at least attempt to recognize 
>these dependent effects.
>Best and hope all had wonderful Holidays,
>-----Original Message-----
>Dear Hugh,
>It sounds to me that you want to do something like Becker (1988) 
>describes in section 5.2. Then you would use the squared standard error 
>of the predicted value (from the meta-regression model) as the sampling 
>variance of the control group estimate of the standardized mean change.
>There is an additional complication that there is then a dependency 
>between the effect sizes (i.e., the difference in the standardized mean 
>change between the treatment and control group) for studies with both 
>treatment and control groups and effect sizes for studies with just a 
>treatment group (and also between the effect sizes from studies with 
>treatment groups only). These covariances can be computed as described 
>5.2 and would need to be put into the 'V' matrix of rma.mv() (if you 
>intend to use metafor). Actually implemting this would require a bit of 
>work though. Showing how to do this with metafor would be a nice little 
>exercise/project for a motivated student.
>-----Original Message-----
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-
>project.org] On Behalf Of Crean, Hugh
>Sent: Sunday, 11 November, 2018 23:22
>To: 'r-sig-meta-analysis using r-project.org'
>Subject: [R-meta] variance of predicted effect sizes
>Colleagues and myself are working on a meta-analysis of sleep 
>interventions.  Many of the studies are only single arm pre-post 
>studies and we are following the advice of Becker (1988) and Morris and 
>(2002) to impute missing control group effect sizes.  We are planning 
>on using meta regression to compute predicted effect sizes for those 
>studies missing control information.  However, I cannot quite figure 
>how to compute and/or get the standard error for this estimate.  Would 
>one run a simple meta- analysis on the predicted scores for those with 
>the data and use the provided se (and variance)?
>Thanks in advance,
>Hugh F. Crean, Ph.D.
>School of Nursing
>University of Rochester
>601 Elmwood Avenue
>Rochester, New York  14620
>(585) 276-5575

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