Skip to contents

Converting binary data

convert_bin.Rd

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

Usage

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

Arguments

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 \(=\frac{m1}{n1}-\frac{m2}{n2}\).

Value

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.

Details

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

References

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

Examples

require("flexmeta")
#> Loading required package: flexmeta
#> Warning: there is no package called '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)
#>             y        se
#> 1   2.0989861 0.9847737
#> 2   3.3570262 1.0165653
#> 3   1.5652318 0.5747840
#> 4   0.8640463 0.4042977
#> 5   1.4656407 0.6332968
#> 6   2.2325713 0.4481371
#> 7   1.5465488 0.7782417
#> 8   0.4125323 0.3721812
#> 9   2.1202635 0.8265438
#> 10  1.8071598 0.6022988
#> 11  2.7646729 0.5382109
#> 12 -0.3544099 0.5555283
#> 13  0.2058521 0.4541130