dat.lee2004.RdResults from studies examining the effectiveness of wrist acupuncture point P6 stimulation for preventing postoperative nausea.
dat.lee2004The data frame contains the following columns:
| id | numeric | trial id number |
| study | character | first author |
| year | numeric | study year |
| ai | numeric | number of patients experiencing nausea in the treatment group |
| n1i | numeric | total number of patients in treatment group |
| ci | numeric | number of patients experiencing nausea in the sham group |
| n2i | numeric | total number of patients in the sham group |
Postoperative nausea and vomiting are common complications following surgery and anaesthesia. As an alternative to drug therapy, acupuncture has been studied as a potential treatment in several trials. The dataset contains the results from 16 clinical trials examining the effectiveness of wrist acupuncture point P6 stimulation for preventing postoperative nausea.
Lee, A., & Done, M. L. (2004). Stimulation of the wrist acupuncture point P6 for preventing postoperative nausea and vomiting. Cochrane Database of Systematic Reviews, 3, CD003281. https://doi.org/10.1002/14651858.CD003281.pub2
medicine, alternative medicine, risk ratios
### copy data into 'dat' and examine data
dat <- dat.lee2004
dat
#> id study year ai n1i ci n2i
#> 1 1 Agarwal 2000 18 100 20 100
#> 2 2 Agarwal 2002 5 50 18 50
#> 3 3 Alkaissi 1999 9 20 7 20
#> 4 4 Alkaissi 2002 32 135 31 139
#> 5 5 Allen 1994 9 23 10 23
#> 6 6 Andrzejowski 1996 11 18 12 18
#> 7 7 Duggal 1998 69 122 80 122
#> 8 8 Dundee 1986 3 25 12 25
#> 9 9 Ferrera-Love 1996 1 30 1 30
#> 10 10 Gieron 1993 11 30 19 30
#> 11 11 Harmon 1999 7 44 16 39
#> 12 12 Harmon 2000 4 47 6 47
#> 13 13 Ho 1996 1 30 13 30
#> 14 14 Rusy 2002 24 40 71 80
#> 15 15 Wang 2002 16 50 53 88
#> 16 16 Zarate 2001 28 110 25 111
### load metafor package
library(metafor)
### meta-analysis based on log risk ratios
res <- rma(measure="RR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat)
res
#>
#> Random-Effects Model (k = 16; tau^2 estimator: REML)
#>
#> tau^2 (estimated amount of total heterogeneity): 0.0554 (SE = 0.0487)
#> tau (square root of estimated tau^2 value): 0.2353
#> I^2 (total heterogeneity / total variability): 46.04%
#> H^2 (total variability / sampling variability): 1.85
#>
#> Test for Heterogeneity:
#> Q(df = 15) = 30.2238, p-val = 0.0111
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> -0.3145 0.0977 -3.2200 0.0013 -0.5059 -0.1231 **
#>
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
predict(res, transf=exp, digits=2)
#>
#> pred ci.lb ci.ub pi.lb pi.ub
#> 0.73 0.60 0.88 0.44 1.20
#>