[R-meta] Data Extraction

Tue, Aug 18, 2020 2:58 AM

se.from.p() back-calculates the SE based on a z-test, when we can probably safely assume that something more along the lines of a t-test was done. But we can do something along the same lines, but using a t-test, with:

1.88 / qt(.64/2, df=30+36-2, lower.tail=FALSE)

This yields 4.000567 for the SE. Using the back-calculation based on the CI, i.e.,

(9.3 - -5.7) / (2*qt(.975, df=30+36-2))

yields 3.754262. The discrepancy is quite large and cannot really be explained based on rounding errors. I don't know how that p-value was computed in the original study - the code above assumes that a t-test was done on the change scores for the two groups.

The SD reported by se.from.p() is based on multiplying the SE with sqrt(N), but this isn't appropriate here either. It should be SE / sqrt(1/n1 + 1/n2), so either

1.88 / qt(.64/2, df=30+36-2, lower.tail=FALSE) / sqrt(1/30 + 1/36)

or

(9.3 - -5.7) / (2*qt(.975, df=30+36-2)) / sqrt(1/30 + 1/36)

So either one of these (~16.2 or ~15.2) can be assumed as the SD of the change scores (within each group). If we now assume r = 0.5, then indeed the SD of the change scores is the same as the SD of the raw scores (although I would not call this conservative), so I would do either:

summary(escalc(measure="SMD", m1i=1.88, sd1i=16.2, m2i=0, sd2i=16.2, n1i=30, n2i=36))
summary(escalc(measure="SMD", m1i=1.88, sd1i=15.2, m2i=0, sd2i=15.2, n1i=30, n2i=36))

This isn't all that different from what got, but in my opinion more justifiable.

Best,
Wolfgang

-----Original Message-----
From: Tobias Saueressig [mailto:t.saueressig at gmx.de]
Sent: Tuesday, 18 August, 2020 11:14
To: Viechtbauer, Wolfgang (SP)
Cc: r-sig-meta-analysis at r-project.org
Subject: Aw: RE: RE: [R-meta] Data Extraction

Dear Wolfgang, at all

sorry for that.

I thought this might also a way to do it. Or am I way off?
Between-group change score from baseline to follow-up (9?months) is?1.8(-
5.7, 9.3) (mean and 95%CI) and p-value 0.64;?Sample size is n1 = 30; n2 =
36?(two group RCT).
require(dmetar)
require(esc)
#### Calculate the standard error from the effect size and p-value ####
se.from.p(effect.size = 1.88, p = 0.64, N = 66,effect.size.type=
"difference", calculate.g = FALSE)
#### Results####
EffectSize StandardError StandardDeviation????? LLCI???? ULCI
1?????? 1.88????? 3.871948????????? 31.45585 -5.709017 9.469017
##### Assume that r = 0.5 (conservative) => SD_change = SD_pre_post *
sqrt(2*0.5) ####
esc_mean_se(grp1m = 1.88, grp1se = 3.871948, grp1n = 30,
grp2m = 0, grp2se = 3.871948 , grp2n = 36, es.type = "g")
#### Results ####
Effect Size Calculation for Meta Analysis

???? Conversion: mean and se to effect size Hedges' g
??? Effect Size:?? 0.0845
?Standard Error:?? 0.2473
?????? Variance:?? 0.0612
?????? Lower CI:? -0.4003
?????? Upper CI:?? 0.5692
???????? Weight:? 16.3488

Gesendet:?Dienstag, 18. August 2020 um 08:50 Uhr
Von:?"Viechtbauer, Wolfgang (SP)"
<wolfgang.viechtbauer at maastrichtuniversity.nl>
An:?"Tobias Saueressig" <t.saueressig at gmx.de>
Cc:?"r-sig-meta-analysis at r-project.org" <r-sig-meta-analysis at r-project.org>
Betreff:?RE: RE: [R-meta] Data Extraction
Please always cc the mailing list.

You can include the study if you know (or can guestimate) the pre-post
correlation.

SD of the change scores = SD * sqrt(2*(1-r)),

where SD is the pre or post treatment SD (this assumes that the SD is the
same before and after the treatment). So if you know r, you can easily
recover the SD and standardize in the usual manner.

Best,
Wolfgang

-----Original Message-----
From: Tobias Saueressig [mailto:t.saueressig at gmx.de]
Sent: Tuesday, 18 August, 2020 7:40
To: Viechtbauer, Wolfgang (SP)
Subject: Aw: RE: [R-meta] Data Extraction

Dear Wolfgang,

you are my hero. Thank you.

I am a bit sad that I cannot include the study because it is based on

change

scores and I have used postintervention only.

Gesendet:?Montag, 17. August 2020 um 22:33 Uhr
Von:?"Viechtbauer, Wolfgang (SP)"
<wolfgang.viechtbauer at maastrichtuniversity.nl>
An:?"Tobias Saueressig" <t.saueressig at gmx.de>, "r-sig-meta-analysis at r-
project.org" <r-sig-meta-analysis at r-project.org>
Betreff:?RE: [R-meta] Data Extraction
Dear Tobias,

The difference between the upper and lower CI bounds divided by the
appropriate critical t-value should be *twice* the SE. So:

SE <- (9.3 - -5.7) / (2*1.99)

should be the SE of the difference in the mean change between the two
groups. Dividing this by sqrt(1/n1 + 1/n2) should give you the SD of the
change scores (assuming homoscedasticity of the change variances in the two
groups), so:

SD <- SE / sqrt(1/30 + 1/36)

Hence:

1.8/SD

yields approximately 0.12. Sure you can apply the bias correction, but this
is hardly relevant here.

More importantly, this d-value standardizes the difference between the two
groups based on the (pooled) SD of the change scores. This is not

comparable

to d-values that use the SD of a single time point for the standardization.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-

project.org]

On Behalf Of Tobias Saueressig
Sent: Monday, 17 August, 2020 12:03
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] Data Extraction

Hello,

I have a problem to extract data from one study.? I want to calculate

hedges

g.

I have the following information (2 groups):

Between-group change score from baseline to follow-up (9?months) is?1.8(-
5.7,9.3) (mean and 95%CI) and p-value 0.64

Sample size is n1 = 30; n2 = 36?(two group RCT).

Link:?https://linkinghub.elsevier.com/retrieve/pii/S1063458420309882?(TABL

2 HOOS ADL)

I would do it the following way:

Calculate a SD for both group from the CI. _> SE = (Upper limit-lower
limit)/t-statistic (~2) => SD = SE/squ(1/n1?+ 1/n2)

Then g = d = MD/SD

g* = (1-3/(4*(n1+n2)-9) * g

e.g.

SE = (9.3+5.7)/1.99 = 7.54 => SD = 7.54/squ(1/30+1/36) = 7.54/0.25 = 30.16

g = 1.8/30.16 = 0.06

g* = 0.06 * (1-3/255) = 0.06 * 0.99 = 0.0594

Is that correct?

Best regards

Tobias

[R-meta] Data Extraction

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