Dear all,
I like to thank you all for contributing for my previous questions on meta-analysis and helping me to complete the work.
I wanted to know the method to convert Se(logOR) to Se of SMD, and the other way. Using cochrane notes, I had converted SMd by multiplying by 0.5513 with LogOR. Can you alll suggest the formula in same way?
Dear Paul,
There are several options for handling combinations of dichotomous and continuous data. Generally, it is useful to summarize results from all the relevant, valid studies in a similar way, but this is not always possible. It may be possible to collect missing data from investigators so that this can be done. If not, it may be useful to summarize the data in three ways: by entering the means and standard deviations as continuous outcomes, by entering the counts as dichotomous outcomes and by entering all of the data in text form as ‘Other data’ outcomes.
There are statistical approaches available which will re-express odds ratios as standardized mean differences (and vice versa), allowing dichotomous and continuous data to be pooled together. Based on an assumption that the underlying continuous measurements in each intervention group follow a logistic distribution (which is a symmetrical distribution similar in shape to the normal distribution but with more data in the distributional tails), and that the variability of the outcomes is the same in both treated and control participants, the odds ratios can be re-expressed as a standardized mean difference according to the following simple formula (Chinn 2000):
.
The standard error of the log odds ratio can be converted to the standard error of a standardized mean difference by multiplying by the same constant (√3/ π = 0.5513). Alternatively standardized mean differences can be re-expressed as log odds ratios by multiplying by π/√3 = 1.814. Once standardized mean differences (or log odds ratios) and their standard errors have been computed for all studies in the meta-analysis, they can be combined using the generic inverse-variance method in RevMan. Standard errors can be computed for all studies by entering the data in RevMan as dichotomous and continuous outcome type data, as appropriate, and converting the confidence intervals for the resulting log odds ratios and standardized mean differences into standard errors
http://handbook.cochrane.org/chapter_9/9_4_6_combining_dichotomous_and_continuous_o
utcomes.htm
No. I multiple by 0.367 with Se(LNOR). Detailed formula can be found in Formula (18) and (19) in the following article (Page 452).
Sanchez-Meca J, Marin-Martinez F, Chacon-Moscoso S. Effect-size indices for dichotomized outcomes in meta-analysis. Psychol Methods. 2003 Dec;8(4):448-67.
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