[R-meta] metafor::escalc question: Confidence intervals for differences in standardized mean change -

Hi Dale,

You almost got it. To convert a non-escalc object to an escalc one, you give it the data via the 'data' argument and you must specify the names of the variables for the outcomes and the corresponding sampling variances via arguments 'yi' and 'vi'. In this example, these variables have the same names as those arguments, so it would be:

new_dat <- escalc(data=dat, yi=yi, vi=vi)

And now

summary(new_dat)

works (append=TRUE is the default anyway and you can leave off measure="GEN", since this will be used as the default as well).

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On
Behalf Of Dale Steele
Sent: Saturday, 15 May, 2021 13:21
To: r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] metafor::escalc question: Confidence intervals for
differences in standardized mean change -

Correcting the last 3 lines of the previous message. The CIs
calculated as follows:

dat[c("ci_lb", "ci_ub")] <- dat$yi + c(-1,1) * (qnorm(1 - 0.025) * sqrt(dat$vi))

On Fri, May 14, 2021 at 2:47 PM Dale Steele <dale.w.steele at gmail.com> wrote:

I'm following the example on the metafor website for Computing the
Difference in the Standardized Mean Change
<https://www.metafor-project.org/doku.php/analyses:morris2008>

I'd like to calculate a confidence interval for each of the
differences in the 'dat' dataframe, rather than calculating by hand as
I have attempted below.  As illustrated below,  I tried to convert
'dat' back to an 'escalc' object with:
new_dat <- escalc(measure = "GEN", dat, append = TRUE) with the hope
that summary(new_dat) would generate the desired confidence intervals,
but this failed with a "Error in as.vector(vi) : argument "vi" is
missing, with no default" error.

Is what I'm doing possible?.

Thanks!

Example below:

datT <- data.frame(
  m_pre   = c(30.6, 23.5, 0.5, 53.4, 35.6),
  m_post  = c(38.5, 26.8, 0.7, 75.9, 36.0),
  sd_pre  = c(15.0, 3.1, 0.1, 14.5, 4.7),
  sd_post = c(11.6, 4.1, 0.1, 4.4, 4.6),
  ni      = c(20, 50, 9, 10, 14),
  ri      = c(0.47, 0.64, 0.77, 0.89, 0.44))

datC <- data.frame(
  m_pre   = c(23.1, 24.9, 0.6, 55.7, 34.8),
  m_post  = c(19.7, 25.3, 0.6, 60.7, 33.4),
  sd_pre  = c(13.8, 4.1, 0.2, 17.3, 3.1),
  sd_post = c(14.8, 3.3, 0.2, 17.9, 6.9),
  ni      = c(20, 42, 9, 11, 14),
  ri      = c(0.47, 0.64, 0.77, 0.89, 0.44))

datT <- escalc(measure="SMCR", m1i=m_post, m2i=m_pre, sd1i=sd_pre,
ni=ni, ri=ri, data=datT)
datC <- escalc(measure="SMCR", m1i=m_post, m2i=m_pre, sd1i=sd_pre,
ni=ni, ri=ri, data=datC)

summary(datT) #generates CI's for within-arm standardized differences
summary(datC)

dat <- data.frame(yi = datT$yi - datC$yi, vi = datT$vi + datC$vi)
# dat is no longer an 'escalc' object
# Next line fails with error
new_dat <- escalc(measure = "GEN", dat, append = TRUE)
dat$sei <- sqrt(dat$vi)
dat$zi <- dat$yi/dat$sei
dat$ci_lb <- dat$yi - dat$zi *dat$sei
dat$ci_ub <- dat$yi + dat$zi *dat$sei