R language meta-analysis effect size

Time:2022-8-9

Original link: http://tecdat.cn/?p=23855

When doing a meta-analysis, you will most likely have to use a common measure to calculate or convert effect size to effect size. There are various tools to do this.

Calculate effect size

The R language covers most of the effect size calculation and conversion options to give you a better understanding. For example, to get the effect size from a t-test:

esc_t(t, p, totaln, grp1n, grp2n,

      es.type = c("d", "g", "or", "logit", "r", "cox.or", "cox.log"),

      study = NULL, ...)

You can then calculate the effect size based on the available parameters as follows:

# unequal sample sizes

esc_t(t = 3.3, grp1n = 100, grp2n = 150)



# equal sample size

esc_t(t = 3.3, totaln = 200)

Transform effect size

The software provides several functions to convert one effect size to another effect size: (standard deviation mean log ratio), (standard deviation mean log ratio), (standard deviation mean log r), ( odd ratios) to mean of standard deviations), (transform correlation coefficient r to Fisher's z) and (transform Fisher's z to correlation coefficient r).

work process

The result of the effect size calculation function is returned as a list.

e1 <- esc(grp1yes = 30, grp1no = 50, grp2yes = 40,

              grp2no = 45, study = "Study 1")


e4 <-mean_sd(grp1m = 7, grp1sd = 2, grp1n = 50, grp2m = 9, grp2sd = 3,

                  grp2n = 60, es.type = "logit", study = "Study 4")

_mydat_ now contains a data frame that contains the results of several effect size calculations:

> mydat

R language meta-analysis effect size

The meta-analysis is then calculated as follows (note that the different effect size measures are for demonstration purposes only – in general, you should only have a common effect size to enter a meta-analysis):

rm(yi = es, sei = se, method = "REML", data = mydat)

R language meta-analysis effect size

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