CUSUM for paired binary data as described in Steiner et al. (1999).

pairedbinCUSUM(stsObj, control = list(range=NULL,theta0,theta1,
h1,h2,h11,h22))
pairedbinCUSUM.runlength(p,w1,w2,h1,h2,h11,h22, sparse=FALSE)

## Arguments

stsObj

Object of class sts containing the paired responses for each of the, say n, patients. The observed slot of stsObj is thus a $$n \times 2$$ matrix.

control

Control object as a list containing several parameters.

• rangeVector of indices in the observed slot to monitor.

• theta0In-control parameters of the paired binary CUSUM.

• theta1Out-of-control parameters of the paired binary CUSUM.

• h1Primary control limit (=threshold) of 1st CUSUM.

• h2Primary control limit (=threshold) of 2nd CUSUM.

• h11Secondary limit for 1st CUSUM.

• h22Secondary limit for 2nd CUSUM.

p

Vector giving the probability of the four different possible states, i.e. c((death=0,near-miss=0),(death=1,near-miss=0), (death=0,near-miss=1),(death=1,near-miss=1)).

w1

The parameters w1 and w2 are the sample weights vectors for the two CUSUMs, see eqn. (2) in the paper. We have that w1 is equal to deaths

w2

As for w1

h1

decision barrier for 1st individual cusums

h2

decision barrier for 2nd cusums

h11

together with h22 this makes up the joing decision barriers

h22

together with h11 this makes up the joing decision barriers

sparse

Boolean indicating whether to use sparse matrix computations from the Matrix library (usually much faster!). Default: FALSE.

## Details

For details about the method see the Steiner et al. (1999) reference listed below. Basically, two individual CUSUMs are run each based on a logistic regression model. The combined CUSUM not only signals if one of its two individual CUSUMs signals, but also if the two CUSUMs simultaneously cross the secondary limits.

categoricalCUSUM

## Value

An sts object with observed, alarm, etc. slots trimmed to the control\$range indices.

## References

Steiner, S. H., Cook, R. J., and Farewell, V. T. (1999), Monitoring paired binary surgical outcomes using cumulative sum charts, Statistics in Medicine, 18, pp. 69--86.

## Author

S. Steiner and M. Höhle

## Examples

#Set in-control and out-of-control parameters as in paper
theta0 <- c(-2.3,-4.5,2.5)
theta1 <- c(-1.7,-2.9,2.5)

#Small helper function to compute the paired-binary likelihood
#of the length two vector yz when the true parameters are theta
dPBin <- function(yz,theta) {
exp(dbinom(yz[1],size=1,prob=plogis(theta[1]),log=TRUE) +
dbinom(yz[2],size=1,prob=plogis(theta[2]+theta[3]*yz[1]),log=TRUE))
}

#Likelihood ratio for all four possible configurations
p <- c(dPBin(c(0,0), theta=theta0), dPBin(c(0,1), theta=theta0),
dPBin(c(1,0), theta=theta0), dPBin(c(1,1), theta=theta0))

#Compute ARL using non-sparse matrix operations
if (FALSE) {
pairedbinCUSUM.runlength(p,w1=c(-1,37,-9,29),w2=c(-1,7),h1=70,h2=32,h11=38,h22=17)
}

#Sparse computations don't work on all machines (e.g. the next line
#might lead to an error. If it works this call can be considerably (!) faster
#than the non-sparse call.
if (FALSE) {
pairedbinCUSUM.runlength(p,w1=c(-1,37,-9,29),w2=c(-1,7),h1=70,h2=32,
h11=38,h22=17,sparse=TRUE)
}

#Use paired binary CUSUM on the De Leval et al. (1994) arterial switch
#operation data on 104 newborn babies
data("deleval")

#Switch between death and near misses
observed(deleval) <- observed(deleval)[,c(2,1)]

#Run paired-binary CUSUM without generating alarms.
pb.surv <- pairedbinCUSUM(deleval,control=list(theta0=theta0,
theta1=theta1,h1=Inf,h2=Inf,h11=Inf,h22=Inf))

plot(pb.surv, xaxis.labelFormat=NULL, ylab="CUSUM Statistic")

######################################################################
#Scale the plots so they become comparable to the plots in Steiner et
#al. (1999). To this end a small helper function is defined.
######################################################################

######################################################################
#Log LR for conditional specification of the paired model
######################################################################
LLR.pairedbin <- function(yz,theta0, theta1) {
#In control
alphay0 <- theta0[1] ; alphaz0 <- theta0[2] ; beta0 <- theta0[3]
#Out of control
alphay1 <- theta1[1] ; alphaz1 <- theta1[2] ; beta1 <- theta1[3]
#Likelihood ratios
llry <- (alphay1-alphay0)*yz[1]+log(1+exp(alphay0))-log(1+exp(alphay1))
llrz <- (alphaz1-alphaz0)*yz[2]+log(1+exp(alphaz0+beta0*yz[1]))-
log(1+exp(alphaz1+beta1*yz[1]))
return(c(llry=llry,llrz=llrz))
}

val <- expand.grid(0:1,0:1)
table <- t(apply(val,1, LLR.pairedbin, theta0=theta0, theta1=theta1))
w1 <- min(abs(table[,1]))
w2 <- min(abs(table[,2]))
S <- upperbound(pb.surv) / cbind(rep(w1,nrow(observed(pb.surv))),w2)

#Show results
par(mfcol=c(2,1))
plot(1:nrow(deleval),S[,1],type="l",main="Near Miss",xlab="Patient No.",
ylab="CUSUM Statistic")
lines(c(0,1e99), c(32,32),lty=2,col=2)
lines(c(0,1e99), c(17,17),lty=2,col=3)

plot(1:nrow(deleval),S[,2],type="l",main="Death",xlab="Patient No.",
ylab="CUSUM Statistic")
lines(c(0,1e99), c(70,70),lty=2,col=2)
lines(c(0,1e99), c(38,38),lty=2,col=3)

######################################################################
# Run the CUSUM with thresholds as in Steiner et al. (1999).
# After each alarm the CUSUM statistic is set to zero and
# monitoring continues from this point. Triangles indicate alarm
# in the respective CUSUM (nearmiss or death). If in both
# simultaneously then an alarm is caued by the secondary limits.
######################################################################
pb.surv2 <- pairedbinCUSUM(deleval,control=list(theta0=theta0,
theta1=theta1,h1=70*w1,h2=32*w2,h11=38*w1,h22=17*w2))

plot(pb.surv2, xaxis.labelFormat=NULL)