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

dat.dorn2007

Format

The data frame contains the following columns:

idnumerictrial id number
studycharacter(first) author
yearnumericpublication year
countrycharactercountry where trial was conducted
ibs.critcharacterIBS diagnostic criteria (Manning, Rome I, Rome II, or Other)
daysnumericnumber of treatment days
visitsnumericnumber of practitioner visits
jadanumericJadad score
x.anumericnumber of responders in the active treatment group
n.anumericnumber of participants in the active treatment group
x.pnumericnumber of responders in the placebo group
n.pnumericnumber 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).

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.

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

Concepts

medicine, alternative medicine, risk ratios

Examples

### copy data into 'dat' and examine data
dat <- dat.dorn2007
dat
#>    id      study year   country ibs.crit days visits jadad x.a n.a x.p n.p
#> 1   1 Bensoussan 1998 Australia   Rome I  112      7     5  29  38  11  32
#> 2   2  Blanchard 1992       USA    Other   84     12     3  20  31  21  30
#> 3   3  Brinkhaus 2005   Germany    Other  126      5     5  14  23  30  46
#> 4   4    Carling 1989    Sweden    Other   14      2     3  17  30   5  13
#> 5   5      Davis 2006        UK  Rome II   30      2     5  11  27   6  23
#> 6   6        Dew 1984     Wales    Other   14     NA     2  24  29   5  29
#> 7   7       Gade 1989   Denmark    Other   28      3     4  26  32   9  22
#> 8   8    Halpern 1996       USA  Manning   42     NA     5  17  18  13  18
#> 9   9        Kim 2003       USA  Rome II   56     NA     4   4  12   5  13
#> 10 10      Leung 2006 Hong Kong  Rome II   56      2     5  21  60  26  59
#> 11 11         Lu 2005 Singapore  Rome II   56     NA     3  15  17   8  17
#> 12 12    Madisch 2004   Germany  Rome II   28      3     4  37  51  20  52
#> 13 13       Nash 1986   England    Other   14     NA     4  13  33  17  33
#> 14 14 Niedzielin 2001    Poland  Manning   28      2     3  19  20   3  20
#> 15 15     Olesen 2000   Denmark  Manning   84      5     5  29  50  30  46
#> 16 16       Rees 1979     Wales    Other   21     NA     1  13  16   5  16
#> 17 17     Sallon 2002    Israel   Rome I   84      4     5  25  33   8  26
#> 18 18      Yadav 1989     India    Other   42     NA     5  37  57  17  50
#> 19 19   Whorwell 2006        UK  Rome II   28      2     5  55  90  37  90

# \dontrun{

### 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
#> 
#> Random-Effects Model (k = 19; tau^2 estimator: DL)
#> 
#> tau^2 (estimated amount of total heterogeneity): 0.14 (SE = 0.07)
#> tau (square root of estimated tau^2 value):      0.38
#> I^2 (total heterogeneity / total variability):   71.15%
#> H^2 (total variability / sampling variability):  3.47
#> 
#> Test for Heterogeneity:
#> Q(df = 18) = 62.40, p-val < .01
#> 
#> Model Results:
#> 
#> estimate    se  zval  pval  ci.lb  ci.ub      
#>     0.41  0.11  3.85  <.01   0.20   0.63  *** 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

### estimated average risk ratio
predict(res, transf=exp)
#> 
#>  pred ci.lb ci.ub pi.lb pi.ub 
#>  1.51  1.23  1.87  0.70  3.25 
#> 

### 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)
#> 
#>  pred ci.lb ci.ub pi.lb pi.ub 
#>  1.47  1.18  1.82  0.67  3.23 
#> 

### 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"))


# }