How to perform a meta-analysis of placebo-controlled trials with the mean ± SD of each group before and after the intervention? I want to do a random-effects meta-analysis (on any programming language) on studies that report data in the following way (numbers were made-up for the example):
"Body weight went from  89.9 ± 6.8 kg to 85.1 ± 7.4 kg in the experimental group (n = 27), and from  87.7 ± 10.5 kg to 83.8 ± 10.0 kg in the placebo group (n = 32)."
I want to answer the question: how much more body weight did the experimental group lose over the placebo group, across all studies?
I don't know how to do that with the data I have. Most meta-analysis tutorials assume you have data in the format "body weight decreased by 0.5 ± 1.1 kg in the experimental group (n = 10), and by 0.8 ± 1.5 kg in the control group (n = 9)." I know how to do a meta-analysis when the data are presented like that. But what if you only have the SDs of each group's body weight before and after the intervention, and not the SDs of the changes in body weight?

This is a common question and there are several ways to tackle this issue. First, I would say that it depends on the study design and the analysis method (MD vs SMD). If this is a randomized trial and you are calculating the pooled mean difference (MD) then the you can just use the post-treatment means in your analysis. Even then, some want to meta-analyze change from baseline values (change scores), even when what is reported are baseline and post-treatment means. The Cochrane Hanbook has covered this topic in detail (https://handbook-5-1.cochrane.org/chapter_16/16_1_3_2_imputing_standard_deviations_for_changes_from_baseline.htm and https://training.cochrane.org/handbook/current/chapter-06#section-6-5-2-8). I posted the link from the older version of the manual as there are R scripts that specifically use it as a reference like this one: https://rdrr.io/github/stamats/MKmisc/man/imputeSD.html. Lastly, I will leave you with this paper as an additional reference: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-018-0483-0.
I'm not going to summarize the chapters here as it is a good read and important that you understand the assumptions being made about the correlation coefficient. In the end, as I mentioned before, if this data is from a randomized trial then I would use the post-treatment means so that you don't have to make assumptions, and perform additional sensitivity analyses to test the robustness of your analyses/ assumptions.
