dat.damico2009.RdResults from 16 studies examining the effectiveness of topical plus systemic antibiotics to prevent respiratory tract infections (RTIs).
dat.damico2009The data frame contains the following columns:
| study | character | first author |
| year | numeric | publication year |
| xt | numeric | number of RTIs in the treatment group |
| nt | numeric | number of patients in the treatment group |
| xc | numeric | number of RTIs in the control group |
| nc | numeric | number of patients in the control group |
| conceal | numeric | allocation concealment (0 = not adequate, 1 = adequate) |
| blind | numeric | blinding (0 = open, 1 = double-blind) |
The dataset includes the results from 16 studies that examined the effectiveness of topical plus systemic antibiotics versus no prophylaxis to prevent respiratory tract infections (RTIs).
D'Amico, R., Pifferi, S., Torri, V., Brazzi, L., Parmelli, E., & Liberati, A. (2009). Antibiotic prophylaxis to reduce respiratory tract infections and mortality in adults receiving intensive care. Cochrane Database of Systematic Reviews, 4, CD000022. https://doi.org/10.1002/14651858.CD000022.pub3
medicine, odds ratios
### copy data into 'dat' and examine data
dat <- dat.damico2009
dat
#> study year xt nt xc nc conceal blind
#> 1 Abele-Horn 1997 13 58 23 30 0 0
#> 2 Aerdts 1991 1 28 29 60 1 0
#> 3 Blair 1991 12 161 38 170 1 0
#> 4 Boland 1991 14 32 17 32 0 1
#> 5 Cockerill 1992 4 75 12 75 0 0
#> 6 Finch 1991 4 20 7 24 1 0
#> 7 Jacobs 1992 0 45 4 46 0 0
#> 8 Kerver 1988 5 49 31 47 0 0
#> 9 Palomar 1997 10 50 25 49 0 0
#> 10 Rocha 1992 7 47 25 54 0 1
#> 11 Sanchez-Garcia 1992 32 131 60 140 1 1
#> 12 Stoutenbeek 2007 62 201 100 200 1 0
#> 13 Ulrich 1989 7 55 26 57 1 0
#> 14 Verwaest 1997 22 193 40 185 1 0
#> 15 Winter 1992 3 91 17 92 1 0
#> 16 Krueger 2002 91 265 149 262 1 1
### load metafor package
library(metafor)
### meta-analysis of the (log) odds ratios using the Mantel-Haenszel method
rma.mh(measure="OR", ai=xt, n1i=nt, ci=xc, n2i=nc, data=dat, digits=2)
#>
#> Equal-Effects Model (k = 16)
#>
#> I^2 (total heterogeneity / total variability): 55.61%
#> H^2 (total variability / sampling variability): 2.25
#>
#> Test for Heterogeneity:
#> Q(df = 15) = 33.79, p-val < .01
#>
#> Model Results (log scale):
#>
#> estimate se zval pval ci.lb ci.ub
#> -1.14 0.09 -12.74 <.01 -1.31 -0.96
#>
#> Model Results (OR scale):
#>
#> estimate ci.lb ci.ub
#> 0.32 0.27 0.38
#>
#> Cochran-Mantel-Haenszel Test: CMH = 169.58, df = 1, p-val < 0.01
#> Tarone's Test for Heterogeneity: X^2 = 37.24, df = 15, p-val < 0.01
#>
### calculate log odds ratios and corresponding sampling variances
dat <- escalc(measure="OR", ai=xt, n1i=nt, ci=xc, n2i=nc, data=dat)
### meta-analysis using a random-effects model
res <- rma(yi, vi, data=dat, method="DL")
res
#>
#> Random-Effects Model (k = 16; tau^2 estimator: DL)
#>
#> tau^2 (estimated amount of total heterogeneity): 0.1784 (SE = 0.1362)
#> tau (square root of estimated tau^2 value): 0.4224
#> I^2 (total heterogeneity / total variability): 55.27%
#> H^2 (total variability / sampling variability): 2.24
#>
#> Test for Heterogeneity:
#> Q(df = 15) = 33.5329, p-val = 0.0040
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> -1.2782 0.1572 -8.1295 <.0001 -1.5864 -0.9700 ***
#>
#> ---
#> 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
#> 0.28 0.20 0.38 0.12 0.67
#>