esc-package {esc}

Effect Size Computation for Meta Analysis

Description

This is an R implementation of the web-based 'Practical Meta-Analysis Effect Size Calculator' from David B. Wilson.

Based on the input, the effect size can be returned as standardized mean difference (d), Hedges' g, correlation coefficient effect size r or Fisher's transformation z, odds ratio or log odds effect size.

Return values

The return value of all functions has the same structure:

• The effect size, whether being d, g, r, (Cox) odds ratios or (Cox) logits, is always named es.

• The standard error of the effect size, se.

• The variance of the effect size, var.

• The lower and upper confidence limits ci.lo and ci.hi.

• The weight factor, based on the inverse-variance, w.

• The total sample size totaln.

• The effect size measure, measure, which is typically specified via the es.type-argument.

• Information on the effect-size conversion, info.

• A string with the study name, if the study-argument was specified in function calls.

Correlation Effect Size

If the correlation effect size r is computed, the transformed Fisher's z and their confidence intervals are also returned. The variance and standard error for the correlation effect size r are always based on Fisher's transformation.

Odds Ratio Effect Size

For odds ratios, the variance and standard error are always returned on the log-scale!

Preparing an Effect Size Data Frame for Meta-Analysis

The results of the effect size calculation functions in this package are returned as list with a esc-class attribute. The combine_esc-function takes one or more of these esc-objects and combines them into a data.frame that can be used as argument for further use, for instance with the rma-function.

    e1 <- esc_2x2(grp1yes = 30, grp1no = 50, grp2yes = 40,
                  grp2no = 45, study = "Study 1")
    e2 <- esc_2x2(grp1yes = 30, grp1no = 50, grp2yes = 40, grp2no = 45,
                  es.type = "or", study = "Study 2")
    e3 <- esc_t(p = 0.03, grp1n = 100, grp2n = 150, study = "Study 3")
    e4 <- esc_mean_sd(grp1m = 7, grp1sd = 2, grp1n = 50, grp2m = 9, grp2sd = 3,
                      grp2n = 60, es.type = "logit", study = "Study 4")

    mydat <- combine_esc(e1, e2, e3, e4)

    metafor::rma(yi = es, sei = se, method = "REML", data = mydat)