In the context of converting data for meta-analysis, it is important to consider the relationship between interquartile ranges and standard deviations (SDs). Interquartile ranges provide information about the spread of data, specifically representing where the central 50% of participants' outcomes lie. However, it is essential to note that estimating a SD from an interquartile range is not always possible, especially in situations where the outcome's distribution is skewed.

When sample sizes are large and the distribution of the outcome is similar to a normal distribution, the width of the interquartile range can be approximately equivalent to 1.35 SDs. This approximation holds when certain assumptions are met. However, it is crucial to exercise caution when relying solely on interquartile ranges, as their use instead of SDs often indicates a skewed distribution of the outcome.

To ensure accurate and precise results for your meta-analysis, I recommend utilizing a reliable statistical calculator that applies the appropriate formulas for converting data and calculating the standard deviation. This will help in obtaining robust estimates and conducting a thorough analysis of your data. Mean Variance Estimation (hkbu.edu.hk)

Yes, they can. Although I fully agree with what Alireza Jafari explains above, is the only possibility to develop quantitative meta-analytical models in these specific cases. It can be clarified later in the discussion as a potential limitation. I recomend using the method proposed by Wan et al., as it´s quite straightfoward

Wan, X., Wang, W., Liu, J. et al. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol 14, 135 (2014). https://doi.org/10.1186/1471-2288-14-135