The `estmeansd`

package implements the methods of McGrath et al. (2020) and Cai et al. (2021) for estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis. Specifically, these methods can be applied to studies that report one of the following sets of summary statistics:

- S1: median, minimum and maximum values, and sample size
- S2: median, first and third quartiles, and sample size
- S3: median, minimum and maximum values, first and third quartiles, and sample size

This package also implements the methods described by McGrath et al. (2023) to estimate the standard error of these mean and standard deviation estimators. The estimated standard errors are needed for computing the weights in conventional inverse-variance weighted meta-analysis approaches.

Additionally, the Shiny app estmeansd implements these methods.

Note that the R package metamedian can apply these methods (as well as several others) to perform a meta-analysis. See McGrath et al. (in press) for a guide on using the `metamedian`

package.

You can install the released version of `estmeansd`

from CRAN with:

After installing the `devtools`

package (i.e., calling `install.packages(devtools)`

), the development version of `estmeansd`

can be installed from GitHub with:

Specifically, this package implements the Box-Cox (BC), Quantile Estimation (QE), and Method for Unknown Non-Normal Distributions (MLN) approaches to estimate the sample mean and standard deviation. The BC, QE, and MLN methods can be applied using the `bc.mean.sd()`

`qe.mean.sd()`

, and `mln.mean.sd()`

functions, respectively:

```
library(estmeansd)
set.seed(1)
# BC Method
res_bc <- bc.mean.sd(min.val = 2, med.val = 4, max.val = 9, n = 100)
res_bc
#> $est.mean
#> [1] 4.210971
#>
#> $est.sd
#> [1] 1.337348
# QE Method
res_qe <- qe.mean.sd(min.val = 2, med.val = 4, max.val = 9, n = 100)
res_qe
#> $est.mean
#> [1] 4.347284
#>
#> $est.sd
#> [1] 1.502171
# MLN Method
res_mln <- mln.mean.sd(min.val = 2, med.val = 4, max.val = 9, n = 100)
res_mln
#> $est.mean
#> [1] 4.195238
#>
#> $est.sd
#> [1] 1.294908
```

To estimate the standard error of these mean estimators, we can apply the `get_SE()`

function as follows: