pimeta (version 1.1.3)

convert_bin: Converting binary data

Description

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

the number of successes in treatment group 1

n1

the number of patients in treatment group 1

m2

the number of successes in treatment group 2

n2

the number of patients in treatment group 2

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

• y: the effect size estimates vector.

• se: the within studies standard errors vector.

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

# NOT RUN {
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 <- pimeta::convert_bin(m1, n1, m2, n2, type = "logOR")
# }
# NOT RUN {
pimeta::pima(dat$y, dat$se)
# }