convert_bin: Converting binary data

Description Usage Arguments Details Value References Examples

View source: R/convert-.r

Description

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

Usage

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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}.

Details

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

Value

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

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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")
pimeta::pima(dat$y, dat$se)

pimeta documentation built on Sept. 17, 2019, 5:03 p.m.