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.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
data("Peto1980")
## Not run: 
# compute effect sizes (log odds ratios):
if (require("metafor")) {
  peto.es <- escalc(measure="OR",
                    ai=treat.events,   n1i=treat.cases,
                    ci=control.events, n2i=control.cases,
                    slab=publication, data=Peto1980)
} else print("Sorry, 'metafor' package not installed!")
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)

bayesmeta documentation built on Sept. 8, 2017, 5:04 p.m.