Results from 16 trials examining the effectiveness of intravenous magnesium in the prevention of death following acute myocardial infarction.

dat.egger2001

Format

The data frame contains the following columns:

idnumerictrial id number
studycharacterfirst author or trial name
yearnumericpublication year
ainumericnumber of deaths in the magnesium group
n1inumericnumber of patients in the magnesium group
cinumericnumber of deaths in the control group
n2inumericnumber of patients in the control group

Details

The dataset includes the results from 16 randomized clinical trials that examined the effectiveness of intravenous magnesium in the prevention of death following acute myocardial infarction. Studies 1-7 were included in the meta-analyses by Teo et al. (1991) and Horner (1992) and were combined with the results from the LIMIT-2 trial (Woods et al., 1992) in Yusuf et al. (1993), suggesting that magnesium is an effective treatment for reducing mortality. However, the results from the ISIS-4 mega trial (ISIS-4 Collaborative Group, 1995) indicated no reduction in mortality with magnesium treatment. Publication bias has been suggested as one possible explanation for the conflicting findings (Egger & Davey Smith, 1995).

The present dataset includes some additional trials and are based on Table 18.2 from Egger, Davey Smith, and Altman (2001).

Source

Egger, M., Davey Smith, G., & Altman, D. G. (Eds.) (2001). Systematic reviews in health care: Meta-analysis in context (2nd ed.). London: BMJ Books.

References

Egger, M., & Davey Smith, G. (1995). Misleading meta-analysis: Lessons from “an effective, safe, simple” intervention that wasn't. British Medical Journal, 310(6982), 752--754. https://doi.org/10.1136/bmj.310.6982.752

Horner, S. M. (1992). Efficacy of intravenous magnesium in acute myocardial infarction in reducing arrhythmias and mortality: Meta-analysis of magnesium in acute myocardial infarction. Circulation, 86(3), 774--779. https://doi.org/10.1161/01.cir.86.3.774

ISIS-4 Collaborative Group (1995). ISIS-4: A randomised factorial trial assessing early oral captopril, oral mononitrate, and intravenous magnesium sulphate in 58,050 patients with suspected acute myocardial infarction. Lancet, 345(8951), 669--685. https://doi.org/10.1016/S0140-6736(95)90865-X

Teo, K. K., Yusuf, S., Collins, R., Held, P. H., & Peto, R. (1991). Effects of intravenous magnesium in suspected acute myocardial infarction: Overview of randomised trials. British Medical Journal, 303(6816), 1499--1503. https://doi.org/10.1136/bmj.303.6816.1499

Woods, K. L., Fletcher, S., Roffe, C., & Haider, Y. (1992). Intravenous magnesium sulphate in suspected acute myocardial infarction: Results of the second Leicester Intravenous Magnesium Intervention Trial (LIMIT-2). Lancet, 339(8809), 1553--1558. https://doi.org/10.1016/0140-6736(92)91828-v

Yusuf, S., Teo, K., & Woods, K. (1993). Intravenous magnesium in acute myocardial infarction: An effective, safe, simple, and inexpensive treatment. Circulation, 87(6), 2043--2046. https://doi.org/10.1161/01.cir.87.6.2043

See also

Examples

### copy data into 'dat' and examine data
dat <- dat.egger2001
dat
#>    id        study year   ai   n1i   ci   n2i
#> 1   1       Morton 1984    1    40    2    36
#> 2   2    Rasmussen 1986    9   135   23   135
#> 3   3        Smith 1986    2   200    7   200
#> 4   4      Abraham 1987    1    48    1    46
#> 5   5    Feldstedt 1988   10   150    8   148
#> 6   6     Shechter 1989    1    59    9    56
#> 7   7 Ceremuzynski 1989    1    25    3    23
#> 8   8    Bertschat 1989    0    22    1    21
#> 9   9        Singh 1990    6    76   11    75
#> 10 10      Pereira 1990    1    27    7    27
#> 11 11     Shechter 1991    2    89   12    80
#> 12 12         Golf 1991    5    23   13    33
#> 13 13    Thogersen 1991    4   130    8   122
#> 14 14      LIMIT-2 1992   90  1159  118  1157
#> 15 15     Shechter 1995    4   107   17   108
#> 16 16       ISIS-4 1995 2216 29011 2103 29039

# \dontrun{

### load metafor package
library(metafor)

### meta-analysis of trials 1-7 using Peto's method (as in Teo et al., 1991)
res <- rma.peto(ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=1:7)
print(res, digits=2)
#> 
#> Equal-Effects Model (k = 7)
#> 
#> I^2 (total heterogeneity / total variability):  21.13%
#> H^2 (total variability / sampling variability): 1.27
#> 
#> Test for Heterogeneity: 
#> Q(df = 6) = 7.61, p-val = 0.27
#> 
#> Model Results (log scale):
#> 
#> estimate    se   zval  pval  ci.lb  ci.ub 
#>    -0.80  0.24  -3.39  <.01  -1.26  -0.34 
#> 
#> Model Results (OR scale):
#> 
#> estimate  ci.lb  ci.ub 
#>     0.45   0.28   0.71 
#> 

### meta-analysis of trials 1-7 and LIMIT-2 (as in Yusuf et al., 1993)
res <- rma.peto(ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=c(1:7,14))
print(res, digits=2)
#> 
#> Equal-Effects Model (k = 8)
#> 
#> I^2 (total heterogeneity / total variability):  35.71%
#> H^2 (total variability / sampling variability): 1.56
#> 
#> Test for Heterogeneity: 
#> Q(df = 7) = 10.89, p-val = 0.14
#> 
#> Model Results (log scale):
#> 
#> estimate    se   zval  pval  ci.lb  ci.ub 
#>    -0.44  0.12  -3.52  <.01  -0.68  -0.19 
#> 
#> Model Results (OR scale):
#> 
#> estimate  ci.lb  ci.ub 
#>     0.65   0.51   0.82 
#> 

### meta-analysis of all trials except ISIS-4
res <- rma.peto(ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat, subset=-16)
print(res, digits=2)
#> 
#> Equal-Effects Model (k = 15)
#> 
#> I^2 (total heterogeneity / total variability):  36.34%
#> H^2 (total variability / sampling variability): 1.57
#> 
#> Test for Heterogeneity: 
#> Q(df = 14) = 21.99, p-val = 0.08
#> 
#> Model Results (log scale):
#> 
#> estimate    se   zval  pval  ci.lb  ci.ub 
#>    -0.60  0.11  -5.53  <.01  -0.81  -0.39 
#> 
#> Model Results (OR scale):
#> 
#> estimate  ci.lb  ci.ub 
#>     0.55   0.44   0.68 
#> 
predict(res, transf=exp, digits=2)
#> 
#>  pred ci.lb ci.ub 
#>  0.55  0.44  0.68 
#> 

### meta-analysis of all trials including ISIS-4
res <- rma.peto(ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat)
print(res, digits=2)
#> 
#> Equal-Effects Model (k = 16)
#> 
#> I^2 (total heterogeneity / total variability):  73.12%
#> H^2 (total variability / sampling variability): 3.72
#> 
#> Test for Heterogeneity: 
#> Q(df = 15) = 55.80, p-val < .01
#> 
#> Model Results (log scale):
#> 
#> estimate    se  zval  pval  ci.lb  ci.ub 
#>     0.01  0.03  0.20  0.84  -0.05   0.07 
#> 
#> Model Results (OR scale):
#> 
#> estimate  ci.lb  ci.ub 
#>     1.01   0.95   1.07 
#> 
predict(res, transf=exp, digits=2)
#> 
#>  pred ci.lb ci.ub 
#>  1.01  0.95  1.07 
#> 

### contour-enhanced funnel plot centered at 0
funnel(res, refline=0, level=c(90, 95, 99), shade=c("white", "gray", "darkgray"))


# }