dat.hine1989 {metadat}

Studies on Prophylactic Use of Lidocaine After a Heart Attack

Description

Results from 6 studies evaluating mortality from prophylactic use of lidocaine in acute myocardial infarction.

Usage

dat.hine1989

Format

The data frame contains the following columns:

study	numeric	study number
source	character	source of data
n1i	numeric	number of patients in lidocaine group
n2i	numeric	number of patients in control group
ai	numeric	number of deaths in lidocaine group
ci	numeric	number of deaths in control group

Details

Hine et al. (1989) conducted a meta-analysis of death rates in randomized controlled trials in which prophylactic lidocaine was administered to patients with confirmed or suspected acute myocardial infarction. The dataset describes the mortality at the end of the assigned treatment period for control and intravenous lidocaine treatment groups for six studies. The question of interest is whether there is a detrimental effect of lidocaine. Because the studies were conducted to compare rates of arrhythmias following a heart attack, the studies, taken individually, are too small to detect important differences in mortality rates.

The data in this dataset were obtained from Table I in Normand (1999, p. 322).

Concepts

medicine, cardiology, risk differences

Author(s)

Wolfgang Viechtbauer, [e-mail], https://www.metafor-project.org

Source

Normand, S. T. (1999). Meta-analysis: Formulating, evaluating, combining, and reporting. Statistics in Medicine, 18(3), 321–359. ⁠https://doi.org/10.1002/(sici)1097-0258(19990215)18:3<321::aid-sim28>3.0.co;2-p⁠

References

Hine, L. K., Laird, N., Hewitt, P., & Chalmers, T. C. (1989). Meta-analytic evidence against prophylactic use of lidocaine in acute myocardial infarction. Archives of Internal Medicine, 149(12), 2694–2698. ⁠https://doi.org/10.1001/archinte.1989.00390120056011⁠

Examples

### copy data into 'dat' and examine data
dat <- dat.hine1989
dat

## Not run: 

### load metafor package
library(metafor)

### calculate risk differences and corresponding sampling variances
dat <- escalc(measure="RD", n1i=n1i, n2i=n2i, ai=ai, ci=ci, data=dat)
dat

### meta-analysis of risk differences using a random-effects model
res <- rma(yi, vi, data=dat)
res


## End(Not run)