[R] using metafor for meta-analysis of before-after studies (escalc, SMCC)

[Viechtbauer Wolfgang (STAT)]

Please see my answers below.

Best,
Wolfgang

> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
> On Behalf Of Moon Qiang
> Sent: Thursday, May 16, 2013 19:12
> To: r-help
> Subject: [R] using metafor for meta-analysis of before-after studies
> (escalc, SMCC)
> 
> Hello.
> 
> I am trying to perform meta-analysis on some before-after studies. These
> studies are designed to clarify if there is any significant metabolic
> change before and after an intervention. There is only one group in these
> studies, i.e., no control group. I followed the e-mail communication of
> R-help (https://stat.ethz.ch/pipermail/r-help/2012-April/308946.html ) and
> the Metafor Manual (version 1.8-0, released 2013-04-11, relevant contents
> can be found on pages of 59-61 under 'Outcome Measures for Individual
> Groups '). I made a trial analysis and attached the output here, I wonder
> if anyone can look through it and give me some comments.
>  I have three questions about the analysis:
> 
> 1) Most studies reported the before-and-after raw change as Mean+/-SD, but
> few of them have reported the values of before-intervention (mean_r and
> sd_r) and the values of after-intervention (mean_s and sd_s), and none of
> them reported the r value (correlation for the before- and after-
> intervention measurements). Based on the guideline of the Metafor manual,
> I
> set the raw mean change as m1i (i.e., raw mean change=mean_s=m1i), and set
> the standard deviation of raw change as sd1i (i.e., the standard deviation
> of raw change =sd_s=sd1i), and set all other arguments including m2i,
> sd2i,
> ri as 0, and then calculated the standardized mean change using change
> score (SMCC). I am not sure if all these settings are correct.

This is correct. The escalc() function still will compute (m1i-m2i)/sqrt(sd1i^2 + sd2i^2 - 2*ri*sd1i*sd2i), but since m2i=sd2i=ri=0, this is equivalent to mean_change / SD_change, which is what you want.

Make sure that mean_s is NOT the standard error (SE) of the change scores, but really the SD.

> 2) A few studies have specified individual values of m1i, m2i, sd1i, sd2i
> ,
> but did not report the change score or its sd. So can I set r=0 and use
> these values to calculate SMCC? Since SMCC is not calculated in the same
> way like 1), will this be a problem?

Yes, this will be a problem, since you now really assume that r=0, which is not correct. Maybe you can back-calculate r from other information (e.g., the p or t value from a t-test -- see https://stat.ethz.ch/pipermail/r-help/2012-April/308946.html). Or you could try to get r from the authors (then you could also just directly ask for the change score mean and SD). If that is not successful, you will have to impute some kind of reasonable value for r and do a sensitivity analysis in the end.

> 3) some studies reported the percentage mean changes instead of raw mean
> change (percentage change=(value of after-intervention - value of before
> intervention) / value of before intervention), I think it may not be the
> right way to simply substitute the raw mean change with the percentage
> mean
> changes. Is there any method to deal with this problem?

Don't know anything off the top of my head.

> Any comments are welcome.
> 
> With best regards.
>   ------------------------------
>  Qiang Yue