-----Original Message-----
From: Crean, Hugh [mailto:hugh_crean at URMC.Rochester.edu]
Sent: Wednesday, 02 January, 2019 22:39
To: Viechtbauer, Wolfgang (SP); 'r-sig-meta-analysis at 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,
Hugh
-----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 in
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.
Best,
Wolfgang
-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-
project.org] On Behalf Of Crean, Hugh
Sent: Sunday, 11 November, 2018 23:22
To: 'r-sig-meta-analysis at r-project.org'
Subject: [R-meta] variance of predicted effect sizes
Hello,
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 DeShon
(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
Hugh F. Crean, Ph.D.
School of Nursing
University of Rochester
601 Elmwood Avenue
Rochester, New York 14620
(585) 276-5575