dat.dorn2007 {metadat} | R Documentation |
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, wvb@metafor-project.org, 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)