README.md

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

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

estmeansd documentation built on June 19, 2022, 1:05 a.m.