
dat.gibson2002
author | character |
first author of study | year |
numeric |
publication year |
n1i | numeric |
number of participants in the intervention group | m1i |
numeric |
mean number of days off work/school in the intervention group |
sd1i | numeric |
standard deviation of the number of days off work/school in the intervention group | n2i |
numeric |
number of participants in the control/comparison group |
m2i | numeric |
mean number of days off work/school in the control/comparison group | sd2i |
numeric |
standard deviation of the number of days off work/school in the control/comparison group |
ai | numeric |
number of participants who had one or more days off work/school in the intervention group | bi |
numeric |
number of participants who no days off work/school in the intervention group |
ci | numeric |
number of participants who had one or more days off work/school in the control/comparison group | di |
numeric |
number of participants who no days off work/school in the control/comparison group |
The majority of studies reporting this outcome provided means and standard deviations allowing a meta-analysis of standardized mean differences. Seven studies also reported the number of participants who had one or more days off work/school in each group. These studies could be meta-analyzed using, for example, (log) risk ratios. Finally, one could also consider a combined analysis based on standardized mean differences computed from the means and standard deviations where available and using probit transformed risk differences (which also provide estimates of the standardized mean difference) for the remaining studies.
Some degree of patient education was provided in all studies. In addition, the type
variable indicates what additional intervention components were included in each study:
### load data
dat <- get(data(dat.gibson2002))
### fixed-effects model analysis of the standardized mean differences
dat <- escalc(measure="SMD", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat)
res <- rma(yi, vi, data=dat, method="FE")
print(res, digits=2)
### fixed-effects model analysis of the (log) risk ratios
dat <- escalc(measure="RR", ai=ai, bi=bi, ci=ci, di=di, data=dat)
res <- rma(yi, vi, data=dat, method="FE")
print(res, digits=2)
predict(res, transf=exp, digits=2)
### fixed-effects model analysis of the standardized mean differences and the probit transformed
### risk differences (which also provide estimates of the standardized mean difference)
dat <- escalc(measure="SMD", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat)
dat <- escalc(measure="PBIT", ai=ai, bi=bi, ci=ci, di=di, data=dat, replace=FALSE)
dat
res <- rma(yi, vi, data=dat, method="FE")
print(res, digits=2)
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