Survival in cardiac arrest
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.
data(scd)
This data frame contains the following columns:
Emergency Service Area (strata)
Ambulance station (PSU)
estimated number of cardiac arrests
number reaching hospital alive
Levy and Lemeshow. "Sampling of Populations" (3rd edition). Wiley.
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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