estmeansd: Estimating the Sample Mean and Standard Deviation from Commonly Reported Quantiles in Meta-Analysis

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

Additionally, the Shiny app estmeansd implements these methods.

Installation

You can install the released version of `estmeansd` from CRAN with:

``install.packages("estmeansd")``

After installing the `devtools` package (i.e., calling `install.packages(devtools)`), the development version of `estmeansd` can be installed from GitHub with:

``devtools::install_github("stmcg/estmeansd")``

Usage

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.mean.sd(min.val = 2, med.val = 4, max.val = 9, n = 100) # BC Method
#> \$est.mean
#> [1] 4.210971
#>
#> \$est.sd
#> [1] 1.337348
qe.mean.sd(min.val = 2, med.val = 4, max.val = 9, n = 100) # QE Method
#> \$est.mean
#> [1] 4.347284
#>
#> \$est.sd
#> [1] 1.502171
mln.mean.sd(min.val = 2, med.val = 4, max.val = 9, n = 100) # MLN Method
#> \$est.mean
#> [1] 4.195238
#>
#> \$est.sd
#> [1] 1.294908``````