(t) | D > t)$and can therefore be estimated by a • Kaplan-Meier estimator,$\hat S(u)$• Nelson-Aalen estimator for$R_1(t)\begin{align} \hat R_1(t) & = \sum_i \int_0^t \frac{1}{Y_\bullet (s)} dN_{1i}(s) \end{align} whereY_{\bullet}(t)= \sum_i Y_i(t)such that the estimator is \begin{align} \hat \mu_1(t) & = \int_0^t \hat S(u) d\hat R_1(u). \end{align} Cook & Lawless (1997), and developed further in Gosh & Lin (2000). The variance can be estimated based on the asymptotic expansion of\hat \mu_1(t) - \mu_1(t)\begin{align} & \sum_i \int_0^t \frac{S(s)}{\pi(s)} dM_{i1} - \mu_1(t) \int_0^t \frac{1}{\pi(s)} dM_i^d + \int_0^t \frac{\mu_1(s) }{\pi(s)} dM_i^d, \end{align} with mean-zero processes •M_i^d(t) = N_i^D(t)- \int_0^t Y_i(s) d \Lambda^D(s)$, •$M_{i1}(t) = N_{i1}(t) - \int_0^t Y_{i}(s) dR_1(s)$. as in Gosh & Lin (2000) # Generating data We start by generating some data to illustrate the computation of the marginal mean library(mets) library(timereg) set.seed(1000) # to control output in simulatins for p-values below.  data(base1cumhaz) data(base4cumhaz) data(drcumhaz) ddr <- drcumhaz base1 <- base1cumhaz base4 <- base4cumhaz rr <- simRecurrent(200,base1,death.cumhaz=ddr) rr$x <- rnorm(nrow(rr))
rr$strata <- floor((rr$id-0.01)/100)
dlist(rr,.~id| id %in% c(1,7,9))


The status variable keeps track of the recurrent evnts and their type, and death the timing of death.

To compute the marginal mean we simly estimate the two rates functions of the number of events of interest and death by using the phreg function (to start without covariates). Then the estimates are combined with standard error computation in the recurrentMarginal function

#  to fit non-parametric models with just a baseline
xr <- phreg(Surv(entry,time,status)~cluster(id),data=rr)
dr <- phreg(Surv(entry,time,death)~cluster(id),data=rr)
par(mfrow=c(1,3))
bplot(dr,se=TRUE)
title(main="death")
bplot(xr,se=TRUE)
# robust standard errors
rxr <-   robust.phreg(xr,fixbeta=1)

# marginal mean of expected number of recurrent events
out <- recurrentMarginal(xr,dr)
bplot(out,se=TRUE,ylab="marginal mean",col=2)


We can also extract the estimate in different time-points

summary(out,times=c(1000,2000))


The marginal mean can also be estimated in a stratified case:

xr <- phreg(Surv(entry,time,status)~strata(strata)+cluster(id),data=rr)
dr <- phreg(Surv(entry,time,death)~strata(strata)+cluster(id),data=rr)
par(mfrow=c(1,3))
bplot(dr,se=TRUE)
title(main="death")
bplot(xr,se=TRUE)
rxr <-   robust.phreg(xr,fixbeta=1)

out <- recurrentMarginal(xr,dr)
bplot(out,se=TRUE,ylab="marginal mean",col=1:2)


Furhter, if we adjust for covariates for the two rates we can still do predictions of marginal mean, what can be plotted is the baseline marginal mean, that is for the covariates equal to 0 for both models. Predictions for specific covariates can also be obtained with the recmarg (recurren marginal mean used solely for predictions without standard error computation).

# cox case
xr <- phreg(Surv(entry,time,status)~x+cluster(id),data=rr)
dr <- phreg(Surv(entry,time,death)~x+cluster(id),data=rr)
par(mfrow=c(1,3))
bplot(dr,se=TRUE)
title(main="death")
bplot(xr,se=TRUE)
rxr <- robust.phreg(xr)

out <- recurrentMarginal(xr,dr)
bplot(out,se=TRUE,ylab="marginal mean",col=1:2)

# predictions witout se's
outX <- recmarg(xr,dr,Xr=1,Xd=1)


# Improving efficiency

We now simulate some data where there is strong heterogenity such that we can improve the efficiency for censored survival data. The augmentation is a regression on the history for each subject consisting of the specified terms terms: Nt, Nt2 (Nt squared), expNt (exp(-Nt)), NtexpNt (Nt*exp(-Nt)) or by simply specifying these directly. This was developed in Cortese and Scheike (2022).

rr <- simRecurrentII(200,base1,base4,death.cumhaz=ddr,cens=3/5000,dependence=4,var.z=1)
rr <-  count.history(rr)

rr <- transform(rr,statusD=status)
rr <- dtransform(rr,statusD=3,death==1)
dtable(rr,~statusD+status+death,level=2,response=1)

xr <- phreg(Surv(start,stop,status==1)~cluster(id),data=rr)
dr <- phreg(Surv(start,stop,death)~cluster(id),data=rr)
# marginal mean of expected number of recurrent events
out <- recurrentMarginal(xr,dr)

times <- 500*(1:10)
recEFF1 <- recurrentMarginalAIPCW(Event(start,stop,statusD)~cluster(id),data=rr,times=times,cens.code=0,
death.code=3,cause=1,augment.model=~Nt)
with( recEFF1, cbind(times,muP,semuP,muPAt,semuPAt,semuPAt/semuP))

times <- 500*(1:10)
###recEFF14 <- recurrentMarginalAIPCW(Event(start,stop,statusD)~cluster(id),data=rr,times=times,cens.code=0,
###death.code=3,cause=1,augment.model=~Nt+Nt2+expNt+NtexpNt)
###with(recEFF14,cbind(times,muP,semuP,muPAt,semuPAt,semuPAt/semuP))

recEFF14 <- recurrentMarginalAIPCW(Event(start,stop,statusD)~cluster(id),data=rr,times=times,cens.code=0,
death.code=3,cause=1,augment.model=~Nt+I(Nt^2)+I(exp(-Nt))+ I( Nt*exp(-Nt)))
with(recEFF14,cbind(times,muP,semuP,muPAt,semuPAt,semuPAt/semuP))

bplot(out,se=TRUE,ylab="marginal mean",col=2)
k <- 1
for (t in times) {
ci1 <- c(recEFF1$muPAt[k]-1.96*recEFF1$semuPAt[k],
recEFF1$muPAt[k]+1.96*recEFF1$semuPAt[k])
ci2 <- c(recEFF1$muP[k]-1.96*recEFF1$semuP[k],
recEFF1$muP[k]+1.96*recEFF1$semuP[k])
lines(rep(t,2)-2,ci2,col=2,lty=1,lwd=2)
lines(rep(t,2)+2,ci1,col=1,lty=1,lwd=2)
k <- k+1
}
legend("bottomright",c("Eff-pred"),lty=1,col=c(1,3))


In the case where covariates might be important but we are still interested in the marginal mean we can also augment wrt these covariates

rr <- mets:::simMarginalMeanCox(200,cens=3/5000,Lam1=base1,LamD=ddr,beta1=c(0.3,-0.3),
dtable(rr,~statusD+status+death,level=2,response=1)

times <- seq(500,5000,by=500)
recEFF1x <- recurrentMarginalAIPCW(Event(start,stop,statusD)~cluster(id),data=rr,times=times,
cens.code=0,death.code=3,cause=1,augment.model=~X1+X2)
with(recEFF1x, cbind(muP,muPA,muPAt,semuP,semuPA,semuPAt,semuPAt/semuP))

xr <- phreg(Surv(start,stop,status==1)~cluster(id),data=rr)
dr <- phreg(Surv(start,stop,death)~cluster(id),data=rr)
out <- recurrentMarginal(xr,dr)
mets::summaryTimeobject(out$times,out$mu,times=times,se.mu=outse.mu)  # Regression models for the marginal mean One can also do regression modelling , using the model \begin{align} E(N_1(t) | X) & = \Lambda_0(t) \exp(X^T \beta) \end{align} then Ghost-Lin suggested IPCW score equations that are implemented in the recreg function of mets. First we generate data that from a Ghosh-Lin model with\beta=(-0.3,0.3)$and the baseline given by base1, this is done under the assumption that the death rate given covariates are on Cox form with baseline ddr:  rr <- mets:::simMarginalMeanCox(200,cens=3/5000,Lam1=base1,LamD=ddr,beta1=c(0.3,-0.3),betad=c(-0.3,0.3),k1=0.1) dtable(rr,~statusD+status+death,level=2,response=1) out <- recreg(Event(start,stop,statusD)~X1+X2+cluster(id),data=rr,cause=1,death.code=3,cens.code=0) outs <- recreg(Event(start,stop,statusD)~X1+X2+cluster(id),data=rr,cause=1,death.code=3,cens.code=0, cens.model=~strata(X1,X2)) summary(out)$coef
summary(outs)$coef ## checking baseline par(mfrow=c(1,1)) bplot(out) bplot(outs,add=TRUE,col=2) lines(scalecumhaz(base1,0.1),col=3,lwd=2)  We note that for the extended censoring model we gain a little efficiency and that the estimates are close to the true values. Also possible to do IPCW regression at fixed time-point  outipcw <- recregIPCW(Event(start,stop,statusD)~X1+X2+cluster(id),data=rr,cause=1,death.code=3, cens.code=0,times=2000) outipcws <- recregIPCW(Event(start,stop,statusD)~X1+X2+cluster(id),data=rr,cause=1,death.code=3, cens.code=0,times=2000,cens.model=~strata(X1,X2)) summary(outipcw)$coef
summary(outipcws)coef  We can also do the Mao-Lin type composite outcome where we both count the cause 1 and deaths for example \begin{align} E(N_1(t) + I(D<t,\epsilon=3) | X) & = \Lambda_0(t) \exp(X^T \beta) \end{align}  out <- recreg(Event(start,stop,statusD)~X1+X2+cluster(id),data=rr,cause=c(1,3), death.code=3,cens.code=0) summary(out)coef


Also demonstrate that this can be done with competing risks death (change some of the cause 3 deaths to cause 4) \begin{align} E(w_1 N_1(t) + w_2 I(D<t,\epsilon=3) | X) & = \Lambda_0(t) \exp(X^T \beta) \end{align} and with weights $w_1,w_2$ that follow the causes, here 1 and 3.

 rr$binf <- rbinom(nrow(rr),1,0.5) rr$statusDC <- rr$statusD rr <- dtransform(rr,statusDC=4, statusD==3 & binf==0) rr$weight <- 1
rr <- dtransform(rr,weight=2,statusDC==3)

outC  <- recreg(Event(start,stop,statusDC)~X1+X2+cluster(id),data=rr,cause=c(1,3),
death.code=c(3,4),cens.code=0)
summary(outC)$coef outCW <- recreg(Event(start,stop,statusDC)~X1+X2+cluster(id),data=rr,cause=c(1,3), death.code=c(3,4),cens.code=0,wcomp=c(1,2)) summary(outCW)$coef

bplot(out,ylab="Mean composite")


Predictions and standard errors can be computed via the iid decompositions of the baseline and the regression coefficients. We illustrate this for the standard Ghosh-Lin model and it requires that the model is fitted with the option cox.prep=TRUE

out  <- recreg(Event(start,stop,statusD)~X1+X2+cluster(id),data=rr,cause=1,death.code=3,
cens.code=0,cox.prep=TRUE)
baseiid <- IIDbaseline.cifreg(out,time=3000)
GLprediid(baseiid,rr[1:5,])


The Ghosh-Lin model can be made more efficient by the regression augmentation method. First computing the augmentation and then in a second step the augmented estimator (Cortese and Scheike (2023)):

 outi  <- recreg(Event(start,stop,statusD)~X1+X2+cluster(id),data=rr,cause=1,death.code=3,cens.code=0,
augment.model=~Nt+X1+X2)
summary(outi)$coef outA <- recreg(Event(start,stop,statusD)~X1+X2+cluster(id),data=rr,cause=1,death.code=3,cens.code=0, augment.model=~Nt+X1+X2,augment=outi$lindyn.augment)
summary(outA)$coef  We note that the simple augmentation improves the standard errors as expected. The data was generated assuming independence with previous number of events so it would suffice to augment only with the covariates. # Other marginal properties The mean is a useful summary measure but it is very easy and useful to look at other simple summary measures such as the probability of exceeding$k$events •$P(N_1^*(t) \ge k)$• cumulative incidence of$T_{k} = \inf { t: N_1^*(t)=k }$with competing$D$. that is thus equivalent to a certain cumulative incidence of$T_k$occurring before$D$. We denote this cumulative incidence as$\hat F_k(t)$. We note also that$N_1^(t)^2can be written as \begin{align} \sum_{k=0}^K \int_0^t I(D > s) I(N_1^(s-)=k) f(k) dN_1^(s) \end{align} withf(k)=(k+1)^2 - k^2, such that its mean can be written as \begin{align} \sum_{k=0}^K \int_0^t S(s) f(k) P(N_1^(s-)= k | D \geq s) E( dN_1^(s) | N_1^(s-)=k, D> s) \end{align} and estimated by \begin{align} \tilde \mu_{1,2}(t) & = \sum_{k=0}^K \int_0^t \hat S(s) f(k) \frac{Y_{1\bullet}^k(s)}{Y_\bullet (s)} \frac{1}{Y_{1\bullet}^k(s)} d N_{1\bullet}^k(s)= \sum_{i=1}^n \int_0^t \hat S(s) f(N_{i1}(s-)) \frac{1}{Y_\bullet (s)} d N_{i1}(s), \end{align} That is very similar to the "product-limit" estimator forE( (N_1^*(t))^2 )\begin{align} \hat \mu_{1,2}(t) & = \sum_{k=0}^K k^2 ( \hat F_{k}(t) - \hat F_{k+1}(t) ). \end{align} We use the esimator of the probabilty of exceeding "k" events based on the fact thatI(N_1^(t) \geq k)is equivalent to \begin{align} \int_0^t I(D > s) I(N_1^(s-)=k-1) dN_1^(s), \end{align} suggesting that its mean can be computed as \begin{align} \int_0^t S(s) P(N_1^(s-)= k-1 | D \geq s) E( dN_1^(s) | N_1^(s-)=k-1, D> s) \end{align} and estimated by \begin{align} \tilde F_k(t) = \int_0^t \hat S(s) \frac{Y_{1\bullet}^{k-1}(s)}{Y_\bullet (s)} \frac{1}{Y_{1\bullet}^{k-1}(s)} d N_{1\bullet}^{k-1}(s). \end{align} To compute these estimators we need to set up the data by computing the number of previous events of type "1" by the count.history function ###cor.mat <- corM <- rbind(c(1.0, 0.6, 0.9), c(0.6, 1.0, 0.5), c(0.9, 0.5, 1.0)) rr <- simRecurrentII(200,base1,base4,death.cumhaz=ddr,cens=3/5000,dependence=4,var.z=1) rr <- count.history(rr) dtable(rr,~death+status) oo <- prob.exceedRecurrent(rr,1) bplot(oo)  We can also look at the mean and variance based on the estimators just described par(mfrow=c(1,2)) with(oo,plot(time,mu,col=2,type="l")) # with(oo,plot(time,varN,type="l"))  We could also use the product-limit estimator to estimate the probability of exceeding "k" events, and then standard errors are also returned:  oop <- prob.exceed.recurrent(rr,1) bplot(oo) matlines(ooptimes,oop$prob,type="l") summaryTimeobject(oop$times,oop$prob,se.mu=oop$se.prob,times=1000)


We note from the plot that the estimates are quite similar.

Finally, we make a plot with 95\% confidence intervals

matplot(oop$times,oop$prob,type="l")
for (i in seq(ncol(oop$prob))) plotConfRegion(oop$times,cbind(oop$se.lower[,i],oop$se.upper[,i]),col=i)


# Multiple events

We now generate recurrent events with two types of events. We start by generating data as before where all events are independent.

rr <- simRecurrentII(200,base1,cumhaz2=base4,death.cumhaz=ddr)
rr <-  count.history(rr)
dtable(rr,~death+status)


Based on this we can estimate also the joint distribution function, that is the probability that $(N_1(t) \geq k_1, N_2(t) \geq k_2)$

# Bivariate probability of exceeding
oo <- prob.exceedBiRecurrent(rr,1,2,exceed1=c(1,5),exceed2=c(1,2))
with(oo, matplot(time,pe1e2,type="s"))
nc <- ncol(oo$pe1e2) legend("topleft",legend=colnames(oo$pe1e2),lty=1:nc,col=1:nc)


## Dependence between events: Covariance

The dependence can also be summarised in other ways. For example by computing the covariance and comparing it to the covariance under the assumption of independence among survivors.

Covariance among two types of events \begin{align} \rho(t) & = \frac{ E(N_1^(t) N_2^(t) ) - \mu_1(t) \mu_2(t) }{ \mbox{sd}(N_1^(t)) \mbox{sd}(N_2^(t)) } \end{align} where $E(N_1^(t) N_2^(t))$ can be computed as \begin{align} E(N_1^(t) N_2^(t)) & = E( \int_0^t N_1^(s-) dN_2^(s) ) + E( \int_0^t N_2^(s-) dN_1^(s) ) \end{align}

Recall that we might have a terminal event present such that we only see $N_1^(t \wedge D)$ and $N_2^(t \wedge D)$.

To compute the covariance we thus compute \begin{align} E(\int_0^t N_1^(s-) dN_2^(s) ) & = \sum_k E( \int_0^t k I(N_1^(s-)=k) I(D \geq s) dN_2^(s) ) \end{align} \begin{align} = \sum_k \int_0^t S(s) k P(N_1^(s-)= k | D \geq s) E( dN_2^(s) | N_1^(s-)=k, D \geq s) \end{align} estimated by \begin{align} & \sum_k \int_0^t \hat S(s) k \frac{Y_1^k(s)}{Y_\bullet (s)} \frac{1}{Y_1^k(s)} d \tilde N_{2,k}(s), \end{align} * $Y_j^k(t) = \sum Y_i(t) I( N_{ji}^(s-)=k)$ for $j=1,2$, * $\tilde N_{j,k}(t) = \sum_i \int_0^t I(N_{ij^o}(s-)=k) dN_{ij}(s)$ * $j^o$ gives the other type so that $1^o=2$ and $2^o=1$.

We thus estimate $E(N_1^(t) N_2^(t))$ by \begin{align} \sum_k \int_0^t \hat S(s) k \frac{Y_1^k(s)}{Y_\bullet (s)} \frac{1}{Y_1^k(s)} d \tilde N_{2,k}(s) + \sum_k \int_0^t \hat S(s) k \frac{Y_2^k(s)}{Y_\bullet (s)} \frac{1}{Y_2^k(s)} d \tilde N_{1,k}(s). \end{align}

• Without terminating event covariance is a useful nonparametric measure.
• With terminating event dependence can be generated terminating event.
• In reality what is of interest would be independence among survivors that is if
• $N_1$ is not predicitive for $N_2$ \begin{align} E( dN_2^(t) | N_1^(t-)=k, D \geq t) = E( dN_2^*(t) | D \geq t) \end{align}
• $N_2$ is not predicitive for $N_1$ \begin{align} E( dN_1^(t) | N_2^(t-)=k, D \geq t) = E( dN_1^*(t) | D \geq t) \end{align}

If the two processes are independent among survivors then \begin{align} E( dN_2^(t) | N_1^(t-)=k, D \geq t) = E( dN_2^(t) | D \geq t) \end{align} so \begin{align} E( \int_0^t N_1^(s-) dN_2^(s) ) & = \int_0^t S(s) E(N_1^(s-) | D \geq s) E( dN_2^(s) | D \geq s) \end{align} and \begin{align} \int_0^t \hat S(s) { \sum_k k \frac{Y_1^k(s)}{Y_\bullet (s)} } \frac{1}{Y_\bullet (s)} dN_{2\bullet}(s), \end{align*} where $N_{j\bullet}(t) = \sum_i \int_0^t dN_{j,i}(s)$.

Under the independence $E(N_1^(t) N_2^(t))$ is estimated \begin{align} \int_0^t \hat S(s) { \sum_k k \frac{Y_1^k(s)}{Y_\bullet (s)} } \frac{1}{Y_\bullet (s)} dN_{2\bullet}(s) + \int_0^t \hat S(s) { \sum_k k \frac{Y_2^k(s)}{Y_\bullet (s)} } \frac{1}{Y_\bullet (s)} dN_{1\bullet}(s). \end{align}

Both estimators, $\hat E(N_1^(t) N_2^(t))$ and $\hat E_I(N_1^(t) N_2^(t))$, as well as $\hat E(N_1^(t))$ and $\hat E(N_2^(t))$, have asymptotic expansions that can be written as a sum of iid processes, similarly to the arguments of Ghosh & Lin 2000, $\sum_i \Psi_i(t)$.
We here, however, use a simple block bootstrap to get standard errors.

We can thus estimate the standard errors and of the estimators and their difference $\hat E(N_1^(t) N_2^(t))- \hat E_I(N_1^(t) N_2^(t))$.

Note that we have terms for whether * $N_1$ is predicitive for $N_2$ * N1 -> N2 : $E( \int_0^t N_1^(s-) dN_2^(s) )$ * this is equivalent to a weighted log-rank test * $N_2$ is predicitive for $N_1$ * N2 -> N1 : $E( \int_0^t N_2^(s-) dN_1^(s) )$ * this is equivalent to a weighted log-rank test

rr$strata <- 1 dtable(rr,~death+status) covrp <- covarianceRecurrent(rr,1,2,status="status",death="death", start="entry",stop="time",id="id",names.count="Count") par(mfrow=c(1,3)) plot(covrp) # with strata, each strata in matrix column, provides basis for fast Bootstrap covrpS <- covarianceRecurrentS(rr,1,2,status="status",death="death", start="entry",stop="time",strata="strata",id="id",names.count="Count")  ## Bootstrap standard errors for terms First fitting the model again to get our estimates of interst, and then computing them for some specific time-points times <- seq(500,5000,500) coo1 <- covarianceRecurrent(rr,1,2,status="status",start="entry",stop="time") # mug <- Cpred(cbind(coo1$time,coo1$EN1N2),times)[,2] mui <- Cpred(cbind(coo1$time,coo1$EIN1N2),times)[,2] mu2.1 <- Cpred(cbind(coo1$time,coo1$mu2.1),times)[,2] mu2.i <- Cpred(cbind(coo1$time,coo1$mu2.i),times)[,2] mu1.2 <- Cpred(cbind(coo1$time,coo1$mu1.2),times)[,2] mu1.i <- Cpred(cbind(coo1$time,coo1$mu1.i),times)[,2] cbind(times,mu2.1,mu2.i) cbind(times,mu1.2,mu1.i)  To get the bootstrap standard errors there is a quick memory demanding function (with S for speed and strata) BootcovariancerecurrenceS and slower function that goes through the loops in R Bootcovariancerecurrence. bt1 <- BootcovariancerecurrenceS(rr,1,2,status="status",start="entry",stop="time",K=100,times=times) #bt1 <- Bootcovariancerecurrence(rr,1,2,status="status",start="entry",stop="time",K=K,times=times) BCoutput <- list(bt1=bt1,mug=mug,mui=mui, bse.mug=bt1$se.mug,bse.mui=bt1$se.mui, dmugi=mug-mui, bse.dmugi=apply(bt1$EN1N2-bt1$EIN1N2,1,sd), mu2.1 = mu2.1 , mu2.i = mu2.i , dmu2.i=mu2.1-mu2.i, mu1.2 = mu1.2 , mu1.i = mu1.i , dmu1.i=mu1.2-mu1.i, bse.mu2.1=apply(bt1$mu2.i,1,sd), bse.mu2.1=apply(bt1$mu2.1,1,sd), bse.dmu2.i=apply(bt1$mu2.1-bt1$mu2.i,1,sd), bse.mu1.2=apply(bt1$mu1.2,1,sd), bse.mu1.i=apply(bt1$mu1.i,1,sd), bse.dmu1.i=apply(bt1$mu1.2-bt1$mu1.i,1,sd) )  We then look at the test for overall dependence in the different time-points. We here have no suggestion of dependence. tt <- BCoutput$dmugi/BCoutput$bse.dmugi cbind(times,2*(1-pnorm(abs(tt))))  We can also take out the specific components for whether$N_1$is predictive for$N_2$and vice versa. We here have no suggestion of dependence. t21 <- BCoutput$dmu1.i/BCoutput$bse.dmu1.i t12 <- BCoutput$dmu2.i/BCoutput$bse.dmu2.i cbind(times,2*(1-pnorm(abs(t21))),2*(1-pnorm(abs(t12))))  We finally plot the boostrap samples par(mfrow=c(1,2)) matplot(BCoutput$bt1$time,BCoutput$bt1$EN1N2,type="l",lwd=0.3) matplot(BCoutput$bt1$time,BCoutput$bt1$EIN1N2,type="l",lwd=0.3)  # Looking at other simulations with dependence Using the normally distributed random effects we plot 4 different settings. We have variance$0.5$for all random effects and change the correlation. We let the correlation between the random effect associated with$N_1$and$N_2$be denoted$\rho_{12}$and the correlation between the random effects associated between$N_j$and$D$the terminal event be denoted as$\rho_{j3}$, and organize all correlation in a vector$\rho=(\rho_{12},\rho_{13},\rho_{23})$. • Scenario I$\rho=(0,0.0,0.0)$Independence among all efects.  data(base1cumhaz) data(base4cumhaz) data(drcumhaz) dr <- drcumhaz base1 <- base1cumhaz base4 <- base4cumhaz par(mfrow=c(1,3)) var.z <- c(0.5,0.5,0.5) # death related to both causes in same way cor.mat <- corM <- rbind(c(1.0, 0.0, 0.0), c(0.0, 1.0, 0.0), c(0.0, 0.0, 1.0)) rr <- simRecurrentII(200,base1,base4,death.cumhaz=dr,var.z=var.z,cor.mat=cor.mat,dependence=2) rr <- count.history(rr,types=1:2) cor(attr(rr,"z")) coo <- covarianceRecurrent(rr,1,2,status="status",start="entry",stop="time") plot(coo,main ="Scenario I")  • Scenario II$\rho=(0,0.5,0.5)$Independence among survivors but dependence on terminal event  var.z <- c(0.5,0.5,0.5) # death related to both causes in same way cor.mat <- corM <- rbind(c(1.0, 0.0, 0.5), c(0.0, 1.0, 0.5), c(0.5, 0.5, 1.0)) rr <- simRecurrentII(200,base1,base4,death.cumhaz=dr,var.z=var.z,cor.mat=cor.mat,dependence=2) rr <- count.history(rr,types=1:2) coo <- covarianceRecurrent(rr,1,2,status="status",start="entry",stop="time") par(mfrow=c(1,3)) plot(coo,main ="Scenario II")  • Scenario III$\rho=(0.5,0.5,0.5)$Positive dependence among survivors and dependence on terminal event  var.z <- c(0.5,0.5,0.5) # positive dependence for N1 and N2 all related in same way cor.mat <- corM <- rbind(c(1.0, 0.5, 0.5), c(0.5, 1.0, 0.5), c(0.5, 0.5, 1.0)) rr <- simRecurrentII(200,base1,base4,death.cumhaz=dr,var.z=var.z,cor.mat=cor.mat,dependence=2) rr <- count.history(rr,types=1:2) coo <- covarianceRecurrent(rr,1,2,status="status",start="entry",stop="time") par(mfrow=c(1,3)) plot(coo,main="Scenario III")  • Scenario IV$\rho=(-0.4,0.5,0.5)\$ Negative dependence among survivors and positive dependence on terminal event
  var.z <- c(0.5,0.5,0.5)
# negative dependence for N1 and N2 all related in same way
cor.mat <- corM <- rbind(c(1.0, -0.4, 0.5), c(-0.4, 1.0, 0.5), c(0.5, 0.5, 1.0))
rr <- simRecurrentII(200,base1,base4,death.cumhaz=dr,var.z=var.z,cor.mat=cor.mat,dependence=2)
rr <- count.history(rr,types=1:2)
coo <- covarianceRecurrent(rr,1,2,status="status",start="entry",stop="time")
par(mfrow=c(1,3))
plot(coo,main="Scenario IV")


# SessionInfo

sessionInfo()


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mets documentation built on Jan. 17, 2023, 5:12 p.m.