Clinical study endpoints, including some biomarkers, are frequently analyzed after a log transformation. To calculate study power for detecting a between-treatment difference in the log scale, an estimate of the standard deviation of the log-transformed variable is needed. Often, though, only summary statistics in the original scale including arithmetic means with corresponding standard deviations or sample medians and inter-quartile ranges are found in the literature. In the absence of individual subjects' log-transformed data for directly calculating the sample standard deviation in the log scale, alternative approaches should be applied. This paper presents methods for estimating the standard deviation of a log-transformed variable via the arithmetic means and standard deviations or medians and inter-quartile ranges of the untransformed variable. It further presents methods for constructing the corresponding confidence intervals. A meta-analysis approach, combining data from all sources for more robust estimation, is also discussed. Simulations and examples are provided to assess the performances of these estimates.