Peto1980: Aspirin after myocardial infarction example data

Description Usage Format Details Source References Examples

Description

Numbers of cases (patients) and events (deaths) in treatment and control groups of six studies.

Usage

1
data("Peto1980")

Format

The data frame contains the following columns:

publication character publication identifier
treat.cases numeric number of cases in treatment group
treat.events numeric number of events in treatment group
control.cases numeric number of cases in control group
control.events numeric number of events in control group

Details

Quoting from Brockwell and Gordon (2001): “The collection consists of six studies, each examining the effect of aspirin after myocardial infarction. In each study the number of patients who died after having been given either aspirin or a control drug is recorded.”

Source

S.E. Brockwell, I.R. Gordon. A comparison of statistical methods for meta-analysis. Statistics in Medicine, 20(6):825-840, 2001.

References

R. Peto. Aspirin after myocardial infarction. The Lancet, 315(8179):1172-1173, 1980.

P.C. Elwood, A.L. Cochrane, M.L.Burr, P.M. Sweetnam, G. Williams, E. Welsby, S.J. Hughes, R. Renton. A randomized controlled trial of acetyl salicylic acid in the secondary prevention of mortality from myocardial infarction. British Medical Journal, 1(5905):436-440, 1974.

The Coronary Drug Project Research Group. Aspirin in coronary heart disease. Journal of Chronic Diseases, 29(10):625-642, 1976.

K. Breddin, D. Loew, K. Lechner, K. Ueberla, E. Walter. Secondary prevention of myocardial infarction: a comparison of acetylsalicylic acid, placebo and phenprocoumon. Haemostasis, 9(6):325-344, 1980.

P.C. Elwood, P.M. Sweetnam. Aspirin and secondary mortality after myocardial infarction. The Lancet, 314(8156):1313-1315, 1979.

Aspirin Myocardial Infarction Study Research Group. A randomized, controlled trial of aspirin in persons recovered from myocardial infarction. Journal of the American Medical Association, 243(7):661-669, 1980.

The Persantine-Aspirin Reinfarction Study Research Group. Persantine and aspirin in coronary heart disease. Circulation, 62(3):449-461, 1980.

Examples

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data("Peto1980")
## Not run: 
# compute effect sizes (log odds ratios) from count data
# (using "metafor" package's "escalc()" function):
require("metafor")
peto.es <- escalc(measure="OR",
                  ai=treat.events,   n1i=treat.cases,
                  ci=control.events, n2i=control.cases,
                  slab=publication, data=Peto1980)
print(peto.es)

# check sensitivity to different prior choices:
peto.ma01 <- bayesmeta(peto.es)
peto.ma02 <- bayesmeta(peto.es, tau.prior=function(t){dhalfnormal(t, scale=1)})
 
par(mfrow=c(2,1))
  plot(peto.ma01, which=4, prior=TRUE, taulim=c(0,1), main="uniform prior")
  plot(peto.ma02, which=4, prior=TRUE, taulim=c(0,1), main="half-normal prior")
par(mfrow=c(1,1))

# compare heterogeneity (tau) estimates:
print(rbind("uniform"    =peto.ma01$summary[,"tau"],
            "half-normal"=peto.ma02$summary[,"tau"]))

# compare effect (mu) estimates:
print(rbind("uniform"    =peto.ma01$summary[,"mu"],
            "half-normal"=peto.ma02$summary[,"mu"]))

summary(peto.ma02)
forestplot(peto.ma02)
plot(peto.ma02)

## End(Not run)

Example output

Loading required package: forestplot
Loading required package: grid
Loading required package: magrittr
Loading required package: checkmate
Loading required package: metafor
Loading required package: Matrix
Loading 'metafor' package (version 2.0-0). For an overview 
and introduction to the package please type: help(metafor).
      publication treat.cases treat.events control.cases control.events      yi
1      BrMedJ1974         615           49           624             67 -0.3289
2   JChronDis1976         758           44           771             64 -0.3845
3 Haemostasis1980         317           27           309             32 -0.2158
4      Lancet1979         832          102           850            126 -0.2196
5        JAMA1980         810           85           406             52 -0.2255
6 Circulation1980        2267          246          2257            219  0.1246
      vi
1 0.0389
2 0.0412
3 0.0753
4 0.0205
5 0.0352
6 0.0096
                 mode    median      mean        sd 95% lower 95% upper
uniform     0.1610761 0.1985701 0.2316886 0.1650014         0 0.5143599
half-normal 0.1598917 0.1945806 0.2222327 0.1434300         0 0.4840545
                  mode     median       mean        sd  95% lower  95% upper
uniform     -0.1510129 -0.1642233 -0.1715075 0.1384680 -0.4535975 0.08546363
half-normal -0.1506543 -0.1636282 -0.1706775 0.1323645 -0.4432182 0.07835400
 'bayesmeta' object.
data (6 estimates):
                         y      sigma
BrMedJ1974      -0.3289012 0.19721981
JChronDis1976   -0.3845457 0.20289717
Haemostasis1980 -0.2157625 0.27449120
Lancet1979      -0.2195622 0.14314855
JAMA1980        -0.2254672 0.18761568
Circulation1980  0.1246363 0.09806494

tau prior (proper):
function (t) 
{
    dhalfnormal(t, scale = 1)
}
<bytecode: 0x58fb118>

mu prior (improper):
uniform(min=-Inf, max=Inf)

ML and MAP estimates:
                   tau         mu
ML joint     0.1397203 -0.1607948
ML marginal  0.1610664 -0.1507305
MAP joint    0.1388857 -0.1604910
MAP marginal 0.1598917 -0.1506543

marginal posterior summary:
                tau         mu      theta
mode      0.1598917 -0.1506543 -0.1291908
median    0.1945806 -0.1636282 -0.1569269
mean      0.2222327 -0.1706775 -0.1706775
sd        0.1434300  0.1323645  0.2958280
95% lower 0.0000000 -0.4432182 -0.7918913
95% upper 0.4840545  0.0783540  0.4145445

(quoted intervals are shortest credible intervals.)

Bayes factors:
        tau=0      mu=0
actual     NA        NA
minimum    NA 0.2964268

relative heterogeneity I^2 (posterior median): 0.5786449 

bayesmeta documentation built on Dec. 15, 2020, 5:15 p.m.