Results from 20 trials examining the effectiveness of antithrombotic treatments to prevent strokes in patients with non-valvular atrial fibrillation.

dat.dogliotti2014

Format

The data frame contains the following columns:

studycharacterstudy label
idnumericstudy ID
treatmentcharactertreatment
strokenumericnumber of strokes
totalnumericnumber of individuals

Details

This data set comes from a systematic review aiming to estimate the effects of eight antithrombotic treatments including placebo in reducing the incidence of major thrombotic events in patients with non-valvular atrial fibrillation (Dogliotti et al., 2014).

The review included 20 studies with 79,808 participants, four studies are three-arm studies. The primary outcome is stroke reduction (yes / no).

Source

Dogliotti, A., Paolasso, E., & Giugliano, R. P. (2014). Current and new oral antithrombotics in non-valvular atrial fibrillation: A network meta-analysis of 79808 patients. Heart, 100(5), 396–405. https://doi.org/10.1136/heartjnl-2013-304347

Concepts

medicine, odds ratios, network meta-analysis, Mantel-Haenszel method

Examples

### Show first 7 rows / 3 studies of the dataset
head(dat.dogliotti2014, 7)
#>           study id       treatment stroke total
#> 1 AFASAK-I 1989  1            VKAs      9   335
#> 2 AFASAK-I 1989  1         Aspirin     16   336
#> 3 AFASAK-I 1989  1 Placebo/Control     19   336
#> 4   BAATAF 1990  2            VKAs      3   212
#> 5   BAATAF 1990  2 Placebo/Control     13   208
#> 6     CAFA 1991  3            VKAs      6   187
#> 7     CAFA 1991  3 Placebo/Control      9   191

# \dontrun{

### Load netmeta package
suppressPackageStartupMessages(library(netmeta))

### Print odds ratios and confidence limits with two digits
settings.meta(digits = 2)

### Change appearance of confidence intervals
cilayout("(", "-")

### Transform data from long arm-based format to contrast-based
### format. Argument 'sm' has to be used for odds ratio as summary
### measure; by default the risk ratio is used in the metabin function
### called internally.
pw <- pairwise(treat = treatment, n = total, event = stroke,
  studlab = study, data = dat.dogliotti2014, sm = "OR")

### Print log odds ratios (TE) and standard errors (seTE)
head(pw, 5)[, 1:5]
#>         studlab  treat1          treat2         TE      seTE
#> 1 AFASAK-I 1989    VKAs         Aspirin -0.5939405 0.4240325
#> 2 AFASAK-I 1989    VKAs Placebo/Control -0.7752100 0.4122678
#> 3 AFASAK-I 1989 Aspirin Placebo/Control -0.1812695 0.3484410
#> 4   BAATAF 1990    VKAs Placebo/Control -1.5356718 0.6482047
#> 5     CAFA 1991    VKAs Placebo/Control -0.3999555 0.5373985

### Conduct network meta-analysis (NMA) with placebo as reference
net <- netmeta(pw, ref = "plac")
#> Warning: Comparison with missing TE / seTE or zero seTE not considered in network meta-analysis.
#> Comparison not considered in network meta-analysis:
#>      studlab treat1  treat2 TE seTE
#>  WASPO, 2007   VKAs Aspirin NA   NA
#> 

### Details on excluded study
selvars <- c("studlab", "event1", "n1", "event2", "n2")
subset(pw, studlab == "WASPO, 2007")[, selvars]
#>        studlab event1 n1 event2 n2
#> 28 WASPO, 2007      0 36      0 39

### Show network graph
netgraph(net, seq = "optimal", number = TRUE)


### Conduct Mantel-Haenszel NMA
net.mh <- netmetabin(pw, ref = "plac")
#> Warning: Study 'WASPO, 2007' without any events excluded from network meta-analysis.

### Compare results of inverse variance and Mantel-Haenszel NMA
nb <- netbind(net, net.mh, random = FALSE,
  name = c("Inverse variance", "Mantel-Haenszel"))
forest(nb, xlim = c(0.15, 2), at = c(0.2, 0.5, 1, 2))


### Print and plot results for inverse variance NMA
net
#> Number of studies: k = 19
#> Number of pairwise comparisons: m = 27
#> Number of observations: o = 79733
#> Number of treatments: n = 8
#> Number of designs: d = 10
#> 
#> Common effects model
#> 
#> Treatment estimate (sm = 'OR', comparison: other treatments vs 'Placebo/Control'):
#>                        OR      95%-CI     z  p-value
#> Apixaban             0.33 (0.25-0.44) -7.79 < 0.0001
#> Aspirin              0.78 (0.63-0.96) -2.28   0.0224
#> Aspirin+Clopidogtrel 0.58 (0.45-0.74) -4.26 < 0.0001
#> Dabigatran 110mg     0.38 (0.28-0.52) -5.92 < 0.0001
#> Dabigatran 150mg     0.27 (0.19-0.37) -7.77 < 0.0001
#> Placebo/Control         .           .     .        .
#> Rivaroxaban          0.32 (0.24-0.44) -7.21 < 0.0001
#> VKAs                 0.41 (0.32-0.52) -7.22 < 0.0001
#> 
#> Random effects model
#> 
#> Treatment estimate (sm = 'OR', comparison: other treatments vs 'Placebo/Control'):
#>                        OR      95%-CI     z  p-value
#> Apixaban             0.33 (0.24-0.47) -6.35 < 0.0001
#> Aspirin              0.76 (0.60-0.98) -2.16   0.0310
#> Aspirin+Clopidogtrel 0.59 (0.43-0.82) -3.17   0.0015
#> Dabigatran 110mg     0.38 (0.25-0.57) -4.63 < 0.0001
#> Dabigatran 150mg     0.27 (0.18-0.41) -6.17 < 0.0001
#> Placebo/Control         .           .     .        .
#> Rivaroxaban          0.32 (0.22-0.48) -5.56 < 0.0001
#> VKAs                 0.41 (0.32-0.54) -6.51 < 0.0001
#> 
#> Quantifying heterogeneity / inconsistency:
#> tau^2 = 0.0134; tau = 0.1158; I^2 = 14.7% (0.0%-51.2%)
#> 
#> Tests of heterogeneity (within designs) and inconsistency (between designs):
#>                     Q d.f. p-value
#> Total           18.76   16  0.2815
#> Within designs  13.17   11  0.2827
#> Between designs  5.59    5  0.3480
forest(net)


# }