dat.dorn2007: Studies on Complementary and Alternative Medicine for...

In metadat: Meta-Analysis Datasets

Studies on Complementary and Alternative Medicine for Irritable Bowel Syndrome

Description

Results from 19 trials examining complementary and alternative medicine (CAM) for irritable bowel syndrome (IBS).

Usage

dat.dorn2007

Format

The data frame contains the following columns:

id	numeric	trial id number
study	character	(first) author
year	numeric	publication year
country	character	country where trial was conducted
ibs.crit	character	IBS diagnostic criteria (Manning, Rome I, Rome II, or Other)
days	numeric	number of treatment days
visits	numeric	number of practitioner visits
jada	numeric	Jadad score
x.a	numeric	number of responders in the active treatment group
n.a	numeric	number of participants in the active treatment group
x.p	numeric	number of responders in the placebo group
n.p	numeric	number of participants in the placebo group

Details

The dataset includes the results from 19 randomized clinical trials that examined the effectiveness of complementary and alternative medicine (CAM) for irritable bowel syndrome (IBS).

Concepts

medicine, alternative medicine, risk ratios

Note

The data were extracted from Table I in Dorn et al. (2009). Comparing the funnel plot in Figure 1 with the one obtained below indicates that the data for study 5 (Davis et al., 2006) in the table were not the ones that were used in the actual analyses.

Author(s)

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

Source

Dorn, S. D., Kaptchuk, T. J., Park, J. B., Nguyen, L. T., Canenguez, K., Nam, B. H., Woods, K. B., Conboy, L. A., Stason, W. B., & Lembo, A. J. (2007). A meta-analysis of the placebo response in complementary and alternative medicine trials of irritable bowel syndrome. Neurogastroenterology & Motility, 19(8), 630–637. https://doi.org/10.1111/j.1365-2982.2007.00937.x

Examples

### copy data into 'dat' and examine data
dat <- dat.dorn2007
dat

## Not run: 

### load metafor package
library(metafor)

### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=x.a, n1i=n.a, ci=x.p, n2i=n.p, data=dat)

### random-effects model
res <- rma(yi, vi, data=dat, digits=2, method="DL")
res

### estimated average risk ratio
predict(res, transf=exp)

### funnel plot with study 5 highlighted in red
funnel(res, atransf=exp, at=log(c(.1, .2, .5, 1, 2, 5, 10)),
       ylim=c(0,1), steps=6, las=1, col=ifelse(id == 5, "red", "black"))

### change log risk ratio for study 5
dat$yi[5] <- -0.44

### results are now more in line with what is reported in the paper
### (although the CI in the paper is not wide enough)
res <- rma(yi, vi, data=dat, digits=2, method="DL")
predict(res, transf=exp)

### funnel plot with study 5 highlighted in red
funnel(res, atransf=exp, at=log(c(.1, .2, .5, 1, 2, 5, 10)),
       ylim=c(0,1), steps=6, las=1, col=ifelse(id == 5, "red", "black"))


## End(Not run)

metadat documentation built on April 6, 2022, 5:08 p.m.