# tests/testci.R In survival: Survival Analysis

```options(na.action=na.exclude) # preserve missings
options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
library(survival)
aeq <- function(x,y,...) all.equal(as.vector(x), as.vector(y),...)

#
# Test out the survfit.ci function, which does competing risk
#   estimates
# Replaced by ordinary calls to survfit, with status a factor
#
# First trivial test
tdata <- data.frame(time=c(1,2,2,3,3,3,5,6),
status = c(0,1,0,1,0,1,0,1),
event =  c(1,1,2,2,1,2,3,2),
grp = c(1,2,1,2,1,2,1,2))
fit <- survfit(Surv(time, status*event, type='mstate') ~1, tdata)

byhand <- function() {
#everyone starts in state 0
p1 <- c(1,0,0)

p2 <- c(6/7, 1/7, 0)  # 0-1 transition at time 2
u2 <- matrix(rep(c(1/49, -1/49, 0), each=8), ncol=3) #leverage matrix at time 2
u2[1,] <- 0  #subject 1 is not present
u2[2,1:2] <- u2[2, 1:2] + c(-1/7, 1/7)

p3 <- c((6/7)*(3/5), 1/7, 12/35) # 0-2 transition at time 3, 5 at risk
h3 <- matrix(c(3/5, 0, 2/5, 0,1,0, 0,1,0), byrow=T, ncol=3) #hazard mat
u3 <- u2 %*% h3
u3[4:8,1] <- u3[4:8,1] + p2[1]*2/25
u3[4:8,3] <- u3[4:8,3]  -p2[1]*2/25
u3[4,] <- u3[4,] + c(-p2[1]/5, 0, p2[1]/5)
u3[6,] <- u3[4,]

p6 <- c(0, 1/7, 6/7) # 0-2 at time 6, 1 at risk
h6 <- matrix(c(-1,0,1,0,1,0,0,1,0), byrow=T, ncol=3)
u6 <- cbind(0, u3[,2], -u3[,2])

V <- rbind(0, colSums(u2^2),
colSums(u3^2),
colSums(u3^2),
colSums(u6^2))
list(P=rbind(p1, p2, p3, p3, p6), u2=u2, u3=u3, u6=u6, V=V)
}
bfit <- byhand()
aeq(fit\$pstate, bfit\$P)
aeq(fit\$n.risk[,1], c(8,7,5,2,1))
aeq(fit\$n.event[,2:3], c(0,1,0,0,0, 0,0 ,2,0,1))
aeq(fit\$std^2, bfit\$V)

# Times purposely has values that are before the start, exact, intermediate
#  and after the end of the observed times
sfit <- summary(fit, times=c(0, 1, 3.5, 6, 7), extend=TRUE)
aeq(sfit\$pstate, rbind(c(1,0,0), bfit\$P[c(1,3,5,5),]))
aeq(sfit\$n.risk[,1], c(8,8, 2, 1, 0))
aeq(sfit\$n.event,  matrix(c(0,0,0,0,0, 0,0,1,0,0, 0,0,2,1,0), ncol=3))

#
# For this we need the competing risks MGUS data set, first
#  event
#
tdata <- mgus1[mgus1\$enum==1,]
# Ensure the old-style call using "etype" works (backwards compatability)
fit1 <- survfit(Surv(stop, status) ~ 1, etype=event, tdata)
fit1b <-survfit(Surv(stop, event) ~1, tdata)
indx <- match("call", names(fit1))
all.equal(unclass(fit1)[-indx], unclass(fit1b)[-indx])

# Now get the overall survival, and the hazard for progression
fit2 <- survfit(Surv(stop, status) ~1, tdata)  #overall to "first bad thing"
fit3 <- survfit(Surv(stop, status*(event=='pcm')) ~1, tdata,
type='fleming')
fit4 <- survfit(Surv(stop, status*(event=='death')) ~1, tdata,
type='fleming')

aeq(fit1\$n.risk[,1], fit2\$n.risk)
aeq(rowSums(fit1\$n.event), fit2\$n.event)

# Classic CI formula
#  integral [hazard(t) S(t-0) dt], where S= "survival to first event"
haz1 <- diff(c(0, -log(fit3\$surv))) #Aalen hazard estimate for progression
haz2 <- diff(c(0, -log(fit4\$surv))) #Aalen estimate for death
tsurv <- c(1, fit2\$surv[-length(fit2\$surv)])  #lagged survival
ci1 <- cumsum(haz1 *tsurv)
ci2 <- cumsum(haz2 *tsurv)
aeq(cbind(ci1, ci2), fit1\$pstate[,2:3])

#
# Now, make sure that it works for subgroups
#
fit1 <- survfit(Surv(stop, event) ~ sex, tdata)
fit2 <- survfit(Surv(stop, event) ~ 1, tdata,
subset=(sex=='female'))
fit3 <- survfit(Surv(stop, event) ~ 1, tdata,
subset=(sex=='male'))

aeq(fit2\$pstate, fit1\$pstate[1:fit1\$strata[1],])
aeq(fit2\$std, fit1\$std[1:fit1\$strata[1],])
aeq(fit3\$pstate, fit1\$pstate[-(1:fit1\$strata[1]),])

#  A second test of cumulative incidence
# compare results to Bob Gray's functions
#  The file gray1 is the result of
#    library(cmprsk)
#    tstat <- ifelse(tdata\$status==0, 0, 1+ (tdata\$event=='death'))
#    gray1 <- cuminc(tdata\$stop, tstat)
fit2 <- survfit(Surv(stop, event) ~ 1, tdata)

if (FALSE) {
# lines of the two graphs should overlay
plot(gray1[[1]]\$time, gray1[[1]]\$est, type='l',
ylim=range(c(gray1[[1]]\$est, gray1[[2]]\$est)),
xlab="Time")
lines(gray1[[2]]\$time, gray1[[2]]\$est, lty=2)
matlines(fit2\$time, fit2\$pstate, col=2, lty=1:2, type='s')
}
# To formally match these is a bit of a nuisance.
#  The cuminc function returns a full step function, and survfit only
# the bottoms of the steps.
temp1 <- tapply(gray1[[1]]\$est, gray1[[1]]\$time, max)[-1]  #toss time 0
indx1 <- match(names(temp1), fit2\$time)
aeq(temp1, fit2\$pstate[indx1,2])

```

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survival documentation built on Aug. 24, 2021, 5:06 p.m.