Doubts on data extraction meta-analysis

Dear all,
I am writing as I have some doubts on how to extract data for my first metanalysis. Basically, I have to extract means and standard deviations for all the studies since the outcome is continuous and convert them in the end into standardized mean differences. However, I came across two studies that only report the differences in means for each group with respect to baseline (along with their confidence intervals). I have no means to derive the initial means ans SD for each group. I ask what I should do; is it correct to insert the values as if they were means and let the software calculate the standardized mean differences?
Thanks in advance to whom can help me

Credibly aggregating a heterogeneous collection of studies from a retrospective literature review isn’t as easy as the textbooks or journals make it out to be. You might want to read the following threads, and follow up on a few of theses references.

I may have become too skeptical, but I’ve come to the conclusion that if you can’t do a meta-regression to make an attempt to explain heterogeneity (too few studies), and cannot control for heterogeneity before collecting data (ie a prospective meta-analysis), just combine the p-values of the individual studies to indicate there is indirect evidence of a possible effect.

Effect size MA with very small numbers of studies are misleading in many (maybe even the majority) cases, although I can think of a few examples were effect size aggregation with as few as 5 studies was valuable.

Care needs to be taken with effect size combination methods, as the following threads will show.

I recommend starting with the Senn articles, and then look up the articles by @Sander on the issue of standardized effects.


perhaps anne whitehead’s book on meta-analysis is useful, ie section 9.6 on imputation of the treatment difference and its variance: “When no variance estimates are reported, it may be possible to calculate a value for var(θ̂) from other statistics presented. …”

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