# convert_bin: Converting binary data In pimeta: Prediction Intervals for Random-Effects Meta-Analysis

## Description

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

## Usage

 1 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

• y: the effect size estimates vector.

• se: the within studies standard errors vector.

## 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

 1 2 3 4 5 6 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) 

### Example output

Prediction & Confidence Intervals for Random-Effects Meta-Analysis

A parametric bootstrap prediction and confidence intervals
Heterogeneity variance: DerSimonian-Laird
Variance for average treatment effect: Hartung (Hartung-Knapp)

No. of studies: 13

Average treatment effect [95% prediction interval]:
1.4209 [-0.6243, 3.5473]
d.f.: 12

Average treatment effect [95% confidence interval]:
1.4209 [0.7933, 2.0507]
d.f.: 12

Heterogeneity measure
tau-squared: 0.7176
I-squared:  69.9%


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