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).
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:
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)
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