get_SE.mln.mean.sd: Parametric bootstrap standard error estimation for the method...

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

Parametric bootstrap standard error estimation for the method for unknown non-normal distributions approach

Description

Computes a parametric bootstrap standard error estimate for the method for unknown non-normal distributions approach.

Usage

## S3 method for class 'mln.mean.sd'
get_SE(x, nboot = 1000, shift.when.negative = TRUE, shift.val = 0.5, ...)

Arguments

x	object of class "mln.mean.sd".
nboot	numeric value giving the number of bootstrap replicates. The default is 1000.
shift.when.negative	logical scalar indicating whether to add a constant to the generated sample quantiles if the smallest quantile (i.e., the minimum value in scenarios S1 and S3, the first quartile in scenario S2) is negative. When this argument is set to TRUE, a constant is added such that the smallest quantile equals shift.val. The default is TRUE.
shift.val	numeric value to which the smallest quantile should be shifted to if it is negative (see argument shift.when.negative). This argument is only applicable when shift.when.negative is set to TRUE. The default is 0.5.
...	other arguments.

Value

A list with the following components:

est.se	Estimated standard error of the mean estimator.
boot.means	Bootstrap replicates of the mean estimates.
boot.sds	Bootstrap replicates of the standard deviation estimates.

References

McGrath S., Katzenschlager S., Zimmer A.J., Seitel A., Steele R., and Benedetti A. (2023). Standard error estimation in meta-analysis of studies reporting medians. Statistical Methods in Medical Research. 32(2):373-388.

Cai S., Zhou J., and Pan J. (2021). Estimating the sample mean and standard deviation from order statistics and sample size in meta-analysis. Statistical Methods in Medical Research. 30(12):2701-2719.

See Also

mln.mean.sd

Examples

## Generate S2 summary data
set.seed(1)
n <- 250
x <- stats::rlnorm(n, 5, 0.25)
quants <- stats::quantile(x, probs = c(0, 0.5, 1))

## Estimate the mean and its standard error
res <- mln.mean.sd(min.val = quants[1], med.val = quants[2], max.val = quants[3],
                  n = n)
get_SE(res)$est.se

estmeansd documentation built on May 29, 2024, 7:11 a.m.