What to do in meta-analysis if confidence interval not symmetrical? I'm about to make a meta analysis of a particular topic. I'm using Comprehensive Meta-Analysis (CMA) ver. 3.0 for that reason. However, after I input all the necessary data, the software refused to create the forest plot because the studies included have asymmetrical confidence interval. 
I checked out that the default for 'allowed asymmetric CI ratio' is 1.10. I've changed the ratio to 2.0, which is the maximum input available. Even after that, there is one study that has asymmetrical CI ratio > 2.0.
What I'm asking is:
1. Is there any solution to minimize the asymmetry of the CI of the studies?
2. Is there any solution to alter the 'allowed asymmetric CI ratio' to more than 2.0 so no study will be excluded in forest plot in CMA?
If it turns out that a transformation can minimize the asymmetry of the CI, is the data still valid and presentable?
 A: Firstly, asymmetric confidence intervals such as for odds ratios, hazard ratios, log transformed analyses and many other situations are perfectly appropriate. Usually there's some transformation that makes them almost  symmetrical (up to rounding, not always when the number of events and/or sample size is very small).
Secondly, I suspect the warning indicates that the program is presumably (I don't know for sure,  but the warning does not make sense otherwise) indicates that the program is trying to treat the data from each study as following a N(estimate, SE) distribution. For that purpose it is probably trying to get the SE from the confidence interval width assuming that  a 95% CI is given by estimate $\pm$ 1.96 $\times$ SE. In your case that would seem to be very inappropriate. So, do not ignore the warning.
Thirdly, more appropriate things to do depend on the type of data. E.g. if your estimates are all hazard, odds or rate ratios from large trials with many events, then working on the log-scale would likely solve the issue (one can back transform after there analysis). However,  other situations may require very different solutions and those may not be covered by your software. The book by Borenstein et al. that they recommend on their webpage is a good introduction into the easier use cases. 
