Results from 6 clinical trials examining the effectiveness of adjusted-dose warfarin for preventing strokes in patients with atrial fibrillation.

dat.hart1999

Format

The data frame contains the following columns:

trialnumerictrial number
studycharacterstudy name (abbreviated)
yearnumericpublication year
x1inumericnumber of strokes in the warfarin group
n1inumericnumber of patients in the warfarin group
t1inumerictotal person-time (in years) in the warfarin group
x2inumericnumber of strokes in the placebo/control group
n2inumericnumber of patients in the placebo/control group
t2inumerictotal person-time (in years) in the placebo/control group
compgrpcharactertype of comparison group (placebo or control)
prevtypecharactertype of prevention (primary or secondary)
trinrcharactertarget range for the international normalized ratio (INR)

Details

The 6 studies provide data with respect to the number of strokes in the warfarin and the comparison (placebo or control) group. In addition, the number of patients and the total person-time (in years) is provided for the two groups. The goal of the meta-analysis was to examine the effectiveness of adjusted-dose warfarin for preventing strokes in patients with atrial fibrillation.

Source

Hart, R. G., Benavente, O., McBride, R., & Pearce, L. A. (1999). Antithrombotic therapy to prevent stroke in patients with atrial fibrillation: A meta-analysis. Annals of Internal Medicine, 131(7), 492--501. https://doi.org/10.7326/0003-4819-131-7-199910050-00003

Examples

### copy data into 'dat' and examine data
dat <- dat.hart1999
dat
#>   trial  study year x1i n1i t1i x2i n2i t2i compgrp  prevtype   trinr
#> 1     1 AFASAK 1989   9 335 413  19 336 398 placebo   primary 2.8-4.2
#> 2     2   SPAF 1991   8 210 263  19 211 245 placebo   primary 2.0-4.5
#> 3     3 BAATAF 1990   3 212 487  13 208 435 control   primary 1.5-2.7
#> 4     4   CAFA 1991   6 187 237   9 191 241 placebo   primary 2.0-3.0
#> 5     5 SPINAF 1992   7 281 489  23 290 483 placebo   primary 1.4-2.8
#> 6     6   EAFT 1993  20 225 507  50 214 405 placebo secondary 2.5-4.0

# \dontrun{

### load metafor package
library(metafor)

### calculate log incidence rate ratios and corresponding sampling variances
dat <- escalc(measure="IRR", x1i=x1i, x2i=x2i, t1i=t1i, t2i=t2i, data=dat)
dat
#> 
#>   trial  study year x1i n1i t1i x2i n2i t2i compgrp  prevtype   trinr      yi     vi 
#> 1     1 AFASAK 1989   9 335 413  19 336 398 placebo   primary 2.8-4.2 -0.7842 0.1637 
#> 2     2   SPAF 1991   8 210 263  19 211 245 placebo   primary 2.0-4.5 -0.9359 0.1776 
#> 3     3 BAATAF 1990   3 212 487  13 208 435 control   primary 1.5-2.7 -1.5793 0.4103 
#> 4     4   CAFA 1991   6 187 237   9 191 241 placebo   primary 2.0-3.0 -0.3887 0.2778 
#> 5     5 SPINAF 1992   7 281 489  23 290 483 placebo   primary 1.4-2.8 -1.2019 0.1863 
#> 6     6   EAFT 1993  20 225 507  50 214 405 placebo secondary 2.5-4.0 -1.1409 0.0700 
#> 

### meta-analysis of log incidence rate ratios using a random-effects model
res <- rma(yi, vi, data=dat)
res
#> 
#> Random-Effects Model (k = 6; tau^2 estimator: REML)
#> 
#> tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0987)
#> tau (square root of estimated tau^2 value):      0
#> I^2 (total heterogeneity / total variability):   0.00%
#> H^2 (total variability / sampling variability):  1.00
#> 
#> Test for Heterogeneity:
#> Q(df = 5) = 2.9625, p-val = 0.7058
#> 
#> Model Results:
#> 
#> estimate      se     zval    pval    ci.lb    ci.ub     ​ 
#>  -1.0168  0.1635  -6.2206  <.0001  -1.3372  -0.6964  *** 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

### average incidence rate ratio with 95% CI
predict(res, transf=exp)
#> 
#>    pred  ci.lb  ci.ub  pi.lb  pi.ub 
#>  0.3617 0.2626 0.4984 0.2626 0.4984 
#> 

### forest plot with extra annotations
par(mar=c(5,4,1,2))
forest(res, xlim=c(-11, 5), at=log(c(.05, .25, 1, 4)), atransf=exp,
       slab=paste0(dat$study, " (", dat$year, ")"),
       ilab=cbind(paste(dat$x1i, "/", dat$t1i, sep=" "),
       paste(dat$x2i, "/", dat$t2i, sep=" ")),
       ilab.xpos=c(-6.5,-4), cex=.85, header="Study (Year)")
op <- par(cex=.85, font=2)
text(c(-6.5,-4), 8.5, c("Warfarin", "Control"))
text(c(-6.5,-4), 7.5, c("Strokes / PT", "Strokes / PT"))
segments(x0=-8, y0=8, x1=-2.75, y1=8)

par(op)

### meta-analysis of incidence rate differences using a random-effects model
res <- rma(measure="IRD", x1i=x1i, x2i=x2i, t1i=t1i, t2i=t2i, data=dat)
res
#> 
#> Random-Effects Model (k = 6; tau^2 estimator: REML)
#> 
#> tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0002)
#> tau (square root of estimated tau^2 value):      0.0139
#> I^2 (total heterogeneity / total variability):   50.75%
#> H^2 (total variability / sampling variability):  2.03
#> 
#> Test for Heterogeneity:
#> Q(df = 5) = 10.1606, p-val = 0.0708
#> 
#> Model Results:
#> 
#> estimate      se     zval    pval    ci.lb    ci.ub     ​ 
#>  -0.0339  0.0081  -4.1546  <.0001  -0.0498  -0.0179  *** 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

# }