convert_bin: Converting binary data
In nshi-stat/pimeta: Prediction Intervals for Random-Effects Meta-Analysis

Description Usage Arguments Details Value References Examples

Converting binary outcome data to the effect size estimates and the within studies standard errors vector

1	convert_bin(m1, n1, m2, n2, type = c("logOR", "logRR", "RD"))

`m1`	A vector of the number of successes in experimental group
`n1`	A vector of the number of patients in experimental group
`m2`	A vector of the number of successes in contorol group
`n2`	A vector of the number of patients in contorol group
`type`	the outcome measure for binary outcome data (default = "logOR"). `logOR`: logarithmic odds ratio, which is defined by =\log \frac{(m1+0.5)(n2-m2+0.5)}{(n1-m1+0.5)(m2+0.5)}. `logRR`: logarithmic relative risk, which is defined by =\log \frac{(m1+0.5)(n2+0.5)}{(n1+0.5)(m2+0.5)}. `RD`: risk difference, which is defined by =\frac{m1}{n1}-\frac{m2}{n2}.

This function implements methods for logarithmic odds ratio, logarithmic relative risk, and risk difference described in Hartung & Knapp (2001).

A data.frame of study data.

y: A numeric vector of the effect size estimates.
se: A numeric vector of the within studies standard errors.

Hartung, J., and Knapp, G. (2001). A refined method for the meta-analysis of controlled clinical trials with binary outcome. Stat Med. 20(24): 3875-3889. https://doi.org/10.1002/sim.1009

require("flexmeta")
m1 <- c(15,12,29,42,14,44,14,29,10,17,38,19,21)
n1 <- c(16,16,34,56,22,54,17,58,14,26,44,29,38)
m2 <- c( 9, 1,18,31, 6,17, 7,23, 3, 6,12,22,19)
n2 <- c(16,16,34,56,22,55,15,58,15,27,45,30,38)
dat <- convert_bin(m1, n1, m2, n2, type = "logOR")
print(dat)