scd {survey} R Documentation

## Survival in cardiac arrest

### Description

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.

### Usage

`data(scd)`

### Format

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

### Source

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

### Examples

```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")

# 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=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")

```

[Package survey version 3.18 Index]