metadat (version 1.4-0)

dat.gibson2002: Studies on the Effectiveness of Self-Management Education and Regular Medical Review for Adults with Asthma

Description

Results from 15 trials examining the effectiveness of self-management education and regular medical review for adults with asthma.

Usage

dat.gibson2002

Format

The data frame contains the following columns:

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
type	numeric	numeric code for the intervention type (see ‘Details’)

Author

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

Concepts

medicine, primary care, risk ratios, standardized mean differences

Details

Asthma management guidelines typically recommend for patients to receive education and regular medical review. While self-management programs have been shown to increase patient knowledge, it is less clear to what extent they actually impact health outcomes. The systematic review by Gibson et al. (2002) examined the effectiveness of self-management education and regular medical review for adults with asthma. In each study, participants receiving a certain management intervention were compared against those in a control/comparison group with respect to a variety of health outcomes. One of the outcomes examined in a number of studies was the number of days off work/school.

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:

1. optimal self-management (writing action plan, self-monitoring, regular medical review),

2. self-monitoring and regular medical review,

3. self-monitoring only,

4. regular medical review only,

5. written action plan only.

Examples

### copy data into 'dat' and examine data
dat <- dat.gibson2002
dat

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

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

### fit an equal-effects model to the standardized mean differences (as in Gibson et al., 2002)
res <- rma(yi, vi, data=dat, method="EE")
print(res, digits=2)

### compute log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=ai, bi=bi, ci=ci, di=di, data=dat)
dat

### fit an equal-effects model to the log risk ratios
res <- rma(yi, vi, data=dat, method="EE")
print(res, digits=2)
predict(res, transf=exp, digits=2)

### note: Gibson et al. (2002) used the Mantel-Haenszel method for their analysis
rma.mh(measure="RR", ai=ai, bi=bi, ci=ci, di=di, data=dat, digits=2)

### compute standardized mean differences where possible and otherwise probit transformed
### risk differences (which also provide estimates of the standardized mean differences)
dat <- escalc(measure="SMD", m1i=m1i, sd1i=sd1i, n1i=n1i,
                             m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat, add.measure=TRUE)
dat <- escalc(measure="PBIT", ai=ai, bi=bi, ci=ci, di=di, data=dat, replace=FALSE, add.measure=TRUE)
dat

### fit a random-effects model to these estimates
res <- rma(yi, vi, data=dat)
print(res, digits=2)

### meta-regression model examining if there are systematic differences based on the
### type of measure used (there are only 2 studies where measure="PBIT", so this isn't
### very conclusive here, but shown for illustration purposes)
res <- rma(yi, vi, mods = ~ measure, data=dat)
print(res, digits=2)
predict(res, newmods=1, digits=2)
}