risks: Absolute risks from 7 survival models

Description Usage Format References Examples

Description

This example dataset includes time-to-event outcomes and absolute risks for reproducing the ROC curves in Figure 3 of Saarela & Arjas (2015), please see the example code below.

Usage

1

Format

A data frame containing the elements

tstop

Age at the end of the follow-up (scaled to zero-one interval)

censvar

Case status (0=censoring, 1=CVD event, 2=other death).

tstart

Age at the start of the follow-up (scaled to zero-one interval).

model1

Absolute risk from model 1.

model2

Absolute risk from model 2.

model3

Absolute risk from model 3.

model4

Absolute risk from model 4.

model5

Absolute risk from model 5.

model6

Absolute risk from model 6.

model7

Absolute risk from model 7.

References

Saarela O., Arjas E. (2015). Non-parametric Bayesian hazard regression for chronic disease risk assessment. Scandinavian Journal of Statistics, 42:609–626.

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## Not run: 
rm(list=ls())
library(monoreg)
library(eha)

# Read the example data:

data(risks)
ftime <- (risks$tstop - risks$tstart)/max(risks$tstop - risks$tstart)
ftime <- ifelse(ftime == 0, ftime + 0.00001, ftime)
censvar <- risks$censvar
case <- censvar == 1
dcase <- censvar == 2
a <- risks$tstart
a2 <- a^2
nobs <- nrow(risks)

# Fit a simple parametric model to remove the effect of baseline age:

wmodel <- weibreg(Surv(ftime, case) ~ a + a2, shape=1)
summary(wmodel)
wcoef <- c(coef(wmodel), 0.0)
wlp <- crossprod(t(cbind(a, a2)), wcoef[1:(length(wcoef)-2)])
wpar <- exp(wcoef[(length(wcoef)-1):length(wcoef)])

whaz <- function(x, bz) {
    return(exp(bz) * (wpar[2] / wpar[1]) * (x / wpar[1])^(wpar[2] - 1))
}
chint <- function(x, bz) {
    return(exp(bz) * (x / wpar[1])^wpar[2])
}
croot <- function(x, bz, c) {
    return(chint(x, bz) - c)
}

# Age-matched case-base sample for model validation:

set.seed(1)
crate <- chint(ftime, wlp)
csrate <- cumsum(crate)
m <- sum(case) * 10
persons <- rep(1:nobs, rmultinom(1, m, crate/sum(crate)))
moments <- rep(NA, m)
for (i in 1:m) {
    u <- runif(1, 0.0, crate[persons[i]])
    moments[i] <- uniroot(croot, c(0.0, ftime[persons[i]]), c=u, 
                          bz=wlp[persons[i]])$root
}
plot(ecdf(risks$tstart[case]), pch=20, col='red')
plot(ecdf(risks$tstart[persons]), pch=20, col='blue', add=TRUE)

rate <- whaz(moments, wlp[persons])
mrate <- mean(rate)

d <- c(rep(0, m), rep(1, sum(censvar == 1)), rep(2, sum(censvar == 2)), censvar)
mom <- c(moments, ftime[censvar == 1], ftime[censvar == 2], rep(1.0, nobs))
per <- c(persons, (1:nobs)[censvar == 1], (1:nobs)[censvar == 2], 1:nobs)

include <- rep(c(1,0), c(m + sum(censvar == 1) + sum(censvar == 2), nobs))
predict <- as.numeric(!include)

offset <- log(sum(crate)/(m * whaz(mom, wlp[per])))
moffset <- rep(log(sum(crate)/(m * mrate)), length(mom))

sprob <- 1/exp(offset)
msprob <- 1/exp(moffset)

stz <- getcmat(2)
settozero <- rbind(stz[1,], stz[1,], stz[2:3,], stz[2:3,])
package <- 1:nrow(settozero)
cr <- c(1,0,rep(1,2),rep(0,2))

# Fit models removing the age effect:

agecir <- matrix(NA, nobs, 7)
for (i in 1:7) {
    agecir[,i] <- as.numeric(colMeans(
monosurv(niter=15000, burnin=5000, adapt=5000, refresh=10, thin=5, 
         birthdeath=10, timevar=1, seed=1, rhoa=0.1, rhob=0.1, 
         years=1.0, deltai=0.1, drange=6.0, predict=predict, include=include, 
         casestatus=d, sprob=msprob, offset=NULL, tstart=NULL, 
         axes=cbind(mom, risks[per,paste('model', i, sep='')]), 
         covariates=rep(1.0, length(per)), ccovariates=rep(1.0, length(per)), 
         settozero=settozero, package=package, cr=cr)$risk))
    print(i)
}

# Fit models without removing the age effect:

cir <- matrix(NA, nobs, 7)
for (i in 1:7) {
    cir[,i] <- as.numeric(colMeans(
monosurv(niter=15000, burnin=5000, adapt=5000, refresh=10, thin=5, 
         birthdeath=10, timevar=1, seed=1, rhoa=0.1, rhob=0.1, 
         years=1.0, deltai=0.1, drange=6.0, predict=predict, include=include, 
         casestatus=d, sprob=sprob, offset=NULL, tstart=NULL, 
         axes=cbind(mom, risks[per,paste('model', i, sep='')]), 
         covariates=rep(1.0, length(per)), ccovariates=rep(1.0, length(per)), 
         settozero=settozero, package=package, cr=cr)$risk))
    print(i)
}

# Calculate ROC curves:

for (i in 1:7) {
    probs <- as.numeric(risks[,paste('model', i, sep='')])
    cutoffs <- sort(unique(probs), decreasing=TRUE)
    truepos <- rep(NA, length(cutoffs))
    falsepos <- rep(NA, length(cutoffs))
    auc <- rep(0.0, length(cutoffs))
    for (j in 1:length(cutoffs)) {
        ind <- as.numeric(probs > cutoffs[j])    
        truepos[j] <- sum(ind * agecir[,i])/sum(agecir[,i])
        falsepos[j] <- sum(ind * (1.0 - agecir[,i]))/sum(1.0 - agecir[,i])
        if (j > 1)
            auc[j] = (truepos[j] + truepos[j-1]) * (falsepos[j] - falsepos[j-1])
    }
    auc <- cumsum(auc) * 0.5
    roc <- cbind(cutoffs, truepos, falsepos, auc)
    save(roc, file=paste('ageroc', i, sep=''))
}

for (i in 1:7) {
    probs <- as.numeric(risks[,paste('model', i, sep='')])
    cutoffs <- sort(unique(probs), decreasing=TRUE)
    truepos <- rep(NA, length(cutoffs))
    falsepos <- rep(NA, length(cutoffs))
    auc <- rep(0.0, length(cutoffs))
    for (j in 1:length(cutoffs)) {
        ind <- as.numeric(probs > cutoffs[j])    
        truepos[j] <- sum(ind * cir[,i])/sum(cir[,i])
        falsepos[j] <- sum(ind * (1.0 - cir[,i]))/sum(1.0 - cir[,i])
        if (j > 1)
            auc[j] = (truepos[j] + truepos[j-1]) * (falsepos[j] - falsepos[j-1])
    }
    auc <- cumsum(auc) * 0.5
    roc <- cbind(cutoffs, truepos, falsepos, auc)
    save(roc, file=paste('roc', i, sep=''))
}

# Plot ROC curves:

# postscript(file.path(getwd(), 'rocs.eps'), paper='special', width=10, height=5, 
#            horizontal=FALSE)
op <- par(cex=1, mar=c(3.75,3.75,0.25,0.25), mfrow=c(1,2), mgp=c(2.5,1,0))

plot(1, xlim=c(0,1), ylim=c(0,1), type='n', xlab='False positive fraction', 
     ylab='True positive fraction')
abline(0, 1, lty='dashed')
cols=c('darkgray','red','blue','darkgreen','orange','purple','magenta')
aucs <- NULL
for (i in 1:7) {
    load(file=paste('roc', i, sep=''))
    aucs <- c(aucs, max(roc[,4]))
    lines(roc[,3], roc[,2], type='s', lwd=2, col=cols[i])
    for (j in c(0.05,0.1,0.15,0.2)) {
        tp <- approx(roc[,1], roc[,2], xout=j)$y
        fp <- approx(roc[,1], roc[,3], xout=j)$y
        idx <- nobs - findInterval(j,sort(roc[,1]))
            points(fp, tp, col=cols[i], pch=20)
        if (i == 1)
            text(fp, tp-0.015, labels=j, pos=4, offset=0.25, col=cols[i], 
                 cex=0.9)
    }
}
legend('bottomright', legend=paste('Model ', 1:7, '; AUC=',     
       format(round(aucs, 3), nsmall=3, scientific=FALSE), sep=''), 
        col=cols, lty=rep('solid',7), lwd=rep(2,7))

plot(1, xlim=c(0,1), ylim=c(0,1), type='n', xlab='False positive fraction', 
     ylab='True positive fraction')
abline(0, 1, lty='dashed')
cols=c('darkgray','red','blue','darkgreen','orange','purple','magenta')
aucs <- NULL
for (i in 1:7) {
    load(file=paste('ageroc', i, sep=''))
    aucs <- c(aucs, max(roc[,4]))
    lines(roc[,3], roc[,2], type='s', lwd=2, col=cols[i])
    for (j in c(0.05,0.1,0.15,0.2)) {
        tp <- approx(roc[,1], roc[,2], xout=j)$y
        fp <- approx(roc[,1], roc[,3], xout=j)$y
        idx <- nobs - findInterval(j,sort(roc[,1]))
        points(fp, tp, col=cols[i], pch=20)
        if (i == 1)
            text(fp, tp-0.015, labels=j, pos=4, offset=0.25, col=cols[i], 
                 cex=0.9)
    }
}
legend('bottomright', legend=paste('Model ', 1:7, '; AUC=',     
       format(round(aucs, 3), nsmall=3, scientific=FALSE), sep=''), 
        col=cols, lty=rep('solid',7), lwd=rep(2,7))

par(op)
# dev.off()

## End(Not run)

monoreg documentation built on Nov. 17, 2017, 7:34 a.m.

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