meta_analysis: Random-effects meta-analysis

In statsExpressions: Tidy Dataframes and Expressions with Statistical Details

Random-effects meta-analysis

Description

Parametric, non-parametric, robust, and Bayesian random-effects meta-analysis.

Usage

meta_analysis(
  data,
  type = "parametric",
  random = "mixture",
  k = 2L,
  conf.level = 0.95,
  ...
)

Arguments

data	A data frame. It must contain columns named estimate (effect sizes or outcomes) and std.error (corresponding standard errors). These two columns will be used: as yi and sei arguments in metafor::rma() (for parametric test) or metaplus::metaplus() (for robust test) as y and SE arguments in metaBMA::meta_random() (for Bayesian test).
type	A character specifying the type of statistical approach: "parametric" "nonparametric" "robust" "bayes" You can specify just the initial letter.
random	The type of random effects distribution. One of "normal", "t-dist", "mixture", for standard normal, t-distribution or mixture of normals respectively.
k	Number of digits after decimal point (should be an integer) (Default: k = 2L).
conf.level	Scalar between 0 and 1 (default: ⁠95%⁠ confidence/credible intervals, 0.95). If NULL, no confidence intervals will be computed.
...	Additional arguments passed to the respective meta-analysis function.

Value

The returned tibble data frame can contain some or all of the following columns (the exact columns will depend on the statistical test):

• statistic: the numeric value of a statistic

• df: the numeric value of a parameter being modeled (often degrees of freedom for the test)

• df.error and df: relevant only if the statistic in question has two degrees of freedom (e.g. anova)

• p.value: the two-sided p-value associated with the observed statistic

• method: the name of the inferential statistical test

• estimate: estimated value of the effect size

• conf.low: lower bound for the effect size estimate

• conf.high: upper bound for the effect size estimate

• conf.level: width of the confidence interval

• conf.method: method used to compute confidence interval

• conf.distribution: statistical distribution for the effect

• effectsize: the name of the effect size

• n.obs: number of observations

• expression: pre-formatted expression containing statistical details

For examples, see data frame output vignette.

Random-effects meta-analysis

The table below provides summary about:

• statistical test carried out for inferential statistics

• type of effect size estimate and a measure of uncertainty for this estimate

• functions used internally to compute these details

Hypothesis testing and Effect size estimation

Type	Test	CI available?	Function used
Parametric	Pearson's correlation coefficient	Yes	correlation::correlation()
Non-parametric	Spearman's rank correlation coefficient	Yes	correlation::correlation()
Robust	Winsorized Pearson's correlation coefficient	Yes	correlation::correlation()
Bayesian	Bayesian Pearson's correlation coefficient	Yes	correlation::correlation()

Note

Important: The function assumes that you have already downloaded the needed package ({metafor}, {metaplus}, or {metaBMA}) for meta-analysis. If they are not available, you will be asked to install them.

Examples

# setup
set.seed(123)
library(statsExpressions)



# let's use `mag` dataset from `{metaplus}`
data(mag, package = "metaplus")
dat <- dplyr::rename(mag, estimate = yi, std.error = sei)

# ----------------------- parametric -------------------------------------

meta_analysis(dat)



# ----------------------- robust ----------------------------------

meta_analysis(dat, type = "random", random = "normal")



# ----------------------- Bayesian ----------------------------------

suppressWarnings(meta_analysis(dat, type = "bayes"))

statsExpressions documentation built on Sept. 12, 2023, 5:07 p.m.