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When literature-based meta-analyses involve outcomes with skewed distributions, the best available data can sometimes be a mixture of results presented on the raw scale and results presented on the logarithmic scale. We review and develop methods for transforming between these results for two-group studies, such as clinical trials and prospective or cross-sectional epidemiological studies. These allow meta-analyses to be conducted using all studies and on a common scale. The methods can also be used to produce a meta-analysis of ratios of geometric means when skewed data are reported on the raw scale for every study. We compare three methods, two of which have alternative standard error formulae, in an application and in a series of simulation studies. We conclude that an approach based on a log-normal assumption for the raw data is reasonably robust to different true distributions; and we provide new standard error approximations for this method. An assumption can be made that the standard deviations in the two groups are equal. This increases precision of the estimates, but if incorrect can lead to very misleading results.
Higgins et al. (Wed,) studied this question.