Furthermore, we compare the estimators of the sample mean and standard deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications. For the third scenario, our method still performs very well for both normal data and skewed data. For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data. We demonstrate the performance of the proposed methods through simulation studies for the three frequently encountered scenarios, respectively. Second, we systematically study the sample mean and standard deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials. Inspired by this, we propose a new estimation method by incorporating the sample size. First, we show that the sample standard deviation estimation in Hozo et al.’s method (BMC Med Res Methodol 5:13, 2005) has some serious limitations and is always less satisfactory in practice. In this paper, we propose to improve the existing literature in several directions. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials.
A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials.
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