Dear David Fisher and metan community,

I have seen a question, ask a while ago by Jawdat Abdulla on how to compute 95% confidence intervals. Your answer was:

The new variables that (ad)metan leaves behind in your dataset (named _ES, _seES, _LCI, _UCI and so on) will always be on the normal/interval scale; that is, on the number line from negative-infinity to positive-infinity. For ratio statistics (Odds Ratio, Relative Risk, Hazard Ratio) this means that, for example, _ES contains the log RR. This explains why some of the values in _LCI are negative: they correspond to values less than 1 on the RR scale. This is explained in the Stata help file under the section "Saved Results". The reason for doing this, is so that the results of any analysis are always stored on the same scale, and have consistent interpretations. So, for instance, when forestplot reads in the values, by default it will plot them on the log-scale and the confidence intervals will be symmetric; and you can transform them back to the RR scale with the "eform" option (or similar). Similarly, the variables _ES and _seES allow you to construct confidence intervals reliably, and/or to derive test statistics such as z = _ES/_seES.

I am trying to get the same numbers for Wallace 2009 by hand:



_ES = -.18281308 and _seES = .28836008

I managed to get 0.83 by simple doing exp(_ES) but I cannot manage to get the 95% CI.
This is the definition I know of them. So I tried doing:
exp(-.18281308) + 1.96*exp(.28836008)/sqrt(111+113) which is 1.00 and not 1.47

What am I missing?

Thanks a lot for your time in advance