These data are from Section 12.2 of Levy and Lemeshow. They describe (a possibly apocryphal) study of survival in out-of-hospital cardiac arrest. Two out of five ambulance stations were sampled from each of three emergency service areas.

1 |

This data frame contains the following columns:

- ESA
Emergency Service Area (strata)

- ambulance
Ambulance station (PSU)

- arrests
estimated number of cardiac arrests

- alive
number reaching hospital alive

Levy and Lemeshow. "Sampling of Populations" (3rd edition). Wiley.

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 34 | ```
data(scd)
## survey design objects
scddes<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA,
nest=TRUE, fpc=rep(5,6))
scdnofpc<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA,
nest=TRUE)
# convert to BRR replicate weights
scd2brr <- as.svrepdesign(scdnofpc, type="BRR")
# or to Rao-Wu bootstrap
scd2boot <- as.svrepdesign(scdnofpc, type="subboot")
# use BRR replicate weights from Levy and Lemeshow
repweights<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1),
c(0,1,0,1,1,0))
scdrep<-svrepdesign(data=scd, type="BRR", repweights=repweights)
# ratio estimates
svyratio(~alive, ~arrests, design=scddes)
svyratio(~alive, ~arrests, design=scdnofpc)
svyratio(~alive, ~arrests, design=scd2brr)
svyratio(~alive, ~arrests, design=scd2boot)
svyratio(~alive, ~arrests, design=scdrep)
# or a logistic regression
summary(svyglm(cbind(alive,arrests-alive)~1, family=quasibinomial, design=scdnofpc))
summary(svyglm(cbind(alive,arrests-alive)~1, family=quasibinomial, design=scdrep))
# Because no sampling weights are given, can't compute design effects
# without replacement: use deff="replace"
svymean(~alive+arrests, scddes, deff=TRUE)
svymean(~alive+arrests, scddes, deff="replace")
``` |

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