Adjusted indirect comparisons are a special case of network meta-analyses, where there is no closed loop in the evidence network. For further details, You can refer to several useful websites, books, and articles, as compiled by Google Scholar. For instance, the website from the Cochrane Collaboration is very complete, and this article by Greco et al is also a useful summary.
Focusing on the Bucher method, the idea behind it is quite simple: if you have trials comparing A vs B and providing an effect estimate of such comparison (e.g. such as log risk ratio [logRR] with corresponding variance [var]), and then trials comparing C vs B (e.g. also providing logRR and var), then the indirect comparison effect estimate of A vs C will be the difference between the two effect estimates above (i.e. logRR of A vs C will be yielded b the difference of logRR of A vs B minus logRR of C vs B). The variance will be instead the sum (not the difference!) of the two variances. From the variance you can easily obtain the standard error (SE) as var=SE^2, and the other way around. Finally, from logRR and SE you can get p values and confidence intervals.
Focusing more on your case, you could use simply Excel and a few formulas, for instance using this Excel sheet which was built mainly for educational purposes. However, for consistency and style I recommend you use Stata or R, especially if you want to publish your findings in a scholarly venue.
Given that you may get several ancillary analyses, including evidence networks and funnel plots, the best option in my opinion is to rely on mvmeta (as well as network graphs) in Stata, or netmeta in R. They are both frequentist and almost correspond to the Bucher method when only indirect comparisons are envisioned.
I cannot run it, so I cannot recommend it to you, but Stata should also have a specific command for this, indirect, by Miladinovic et al.
