OK - if the data is from continuous data then one may approximate d by dividing ln Odds Ratio (i.e., Natural Log Odds Ratio) by 1.81 (well, pi divided by the squareroot of 3). See Chinn, S. (2000). A simple method for converting an odds ratio to effect size for use in meta-analysis. Statistic in Medicine, 19, 3127-3131. (I've attached the paper here - might have to remove it though soon due to IP issues so please grab it quick [and then cite it, of course]).

Rearranging the formula would mean that to get an OR from a d then one would:

multiply d by 1.81 to get ln(OR)

antilog the result to get an OR

But - that's a lot of power gone missing (translating a continuous effect size into a binary type effect size).

Does this help?

Would it be useful to have this built into ClinTools do you think?

Best,

Dev