metadat (version 1.4-0)

dat.normand1999: Studies on the Length of Hospital Stay of Stroke Patients

Description

Results from 9 studies on the length of the hospital stay of stroke patients under specialized care and under conventional/routine (non-specialist) care.

Usage

dat.normand1999

Format

The data frame contains the following columns:

study	numeric	study number
source	character	source of data
n1i	numeric	number of patients under specialized care
m1i	numeric	mean length of stay (in days) under specialized care
sd1i	numeric	standard deviation of the length of stay under specialized care
n2i	numeric	number of patients under routine care
m2i	numeric	mean length of stay (in days) under routine care
sd2i	numeric	standard deviation of the length of stay under routine care

Author

Wolfgang Viechtbauer, , https://www.metafor-project.org

Concepts

medicine, raw mean differences, standardized mean differences

Details

The 9 studies provide data in terms of the mean length of the hospital stay (in days) of stroke patients under specialized care and under conventional/routine (non-specialist) care. The goal of the meta-analysis was to examine the hypothesis whether specialist stroke unit care will result in a shorter length of hospitalization compared to routine management.

Examples

### copy data into 'dat' and examine data
dat <- dat.normand1999
dat

if (FALSE) {
### load metafor package
library(metafor)

### calculate mean differences and corresponding sampling variances
dat <- escalc(measure="MD", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat)
dat

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

### meta-analysis of standardized mean differences using a random-effects model
res <- rma(measure="SMD", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i,
           data=dat, slab=source)
res

### draw forest plot
forest(res, xlim=c(-7,5), alim=c(-3,1), header="Study/Source")

### calculate (log transformed) ratios of means and corresponding sampling variances
dat <- escalc(measure="ROM", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat)
dat

### meta-analysis of the (log transformed) ratios of means using a random-effects model
res <- rma(yi, vi, data=dat)
res
predict(res, transf=exp, digits=2)
}