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Recurrent events
Klaus Holst & Thomas Scheike
2024-02-16
Overview
For recurrent events data it is often of interest to compute basic
descriptive quantities to get some basic understanding of the
phenonmenon studied. We here demonstrate how one can compute:
- the marginal mean
- efficient marginal mean estimation
- the Ghosh-Lin Cox type regression for the marginal mean, possibly
with composite outcomes.
- efficient regression augmentation of the Ghosh-Lin model
- the variance of a recurrent events process
- the probability of exceeding k events
- the two-stage recurrent events model
We also show how to improve the efficiency of recurrents events
marginal mean.
In addition several tools can be used for simulating recurrent events
and bivariate recurrent events data, also with a possible terminating
event:
- recurrent events up to two causes and death, given rates of
survivors and death on Cox form.
- the Ghosh-Lin model when the survival rate is on Cox form.
- The general illness death model with cox models for all
hazards.
For bivariate recurrent events we also compute summary measures that
describe their dependence such as
- the covariance
- directional dependence
- the bivariate probability of exceeding \((k_1,k_2)\) events
Simulation of recurrents events
We start by simulating some recurrent events data with two type of
events with cumulative hazards
- \(\Lambda_1(t)\) (rate among
survivors)
- \(\Lambda_2(t)\) (rate among
survivors)
- \(\Lambda_D(t)\)
where we consider types 1 and 2 and with a rate of the terminal event
given by \(\Lambda_D(t)\). We let the
events be independent, but could also specify a random effects structure
to generate dependence.
When simulating data we can impose various random-effects structures
to generate dependence
Dependence=0: The intensities can be independent.
Dependence=1: We can one gamma distributed random effects \(Z\). Then the intensities are
- \(Z \lambda_1(t)\)
- \(Z \lambda_2(t)\)
- \(Z \lambda_D(t)\)
Dependence=2: We can draw normally distributed random effects
\(Z_1,Z_2,Z_d\) were the variance
(var.z) and correlation can be specified (cor.mat). Then the intensities
are
- \(\exp(Z_1) \lambda_1(t)\)
- \(\exp(Z_2) \lambda_2(t)\)
- \(\exp(Z_3) \lambda_D(t)\)
Dependence=3: We can draw gamma distributed random effects \(Z_1,Z_2,Z_d\) were the sum-structure can be
speicifed via a matrix cor.mat. We compute \(\tilde Z_j = \sum_k Z_k^{cor.mat(j,k)}\)
for \(j=1,2,3\).
Then the intensities are
- \(\tilde Z_1 \lambda_1(t)\)
- \(\tilde Z_2 \lambda_2(t)\)
- \(\tilde Z_3 \lambda_D(t)\)
We return to how to run the different set-ups later and start by
simulating independent processes.
The key functions are
- simRecurrent
- simple simulation with only one event type and death
- simRecurrentII
- extended version with possibly multiple types of recurrent events
(but rates can be 0)
- Allows Cox types rates with subject specific rates
In addition we can simulate data from the Ghosh-Lin model and where
marginals of the rates among survivors are on on Cox form
- simGLcox
- can simulate data from Ghosh-Lin model (also simRecurrentCox)
- with frailties
- where survival model for terminal event is on Cox form
- can simulate data where rates among survivors are are con Cox form
see examples below for specific models.
Utility functions
We here mention two utility functions
- tie.breaker for breaking ties among jump-times which is expected in
the functions below.
- count.history that counts the number of jumps previous for each
subject that is \(N_1(t-)\) and \(N_2(t-)\).
Marginal Mean
We start by estimating the marginal mean \(E(N_1(t \wedge D))\) where \(D\) is the timing of the terminal
event.
This is based on a rate model for
- the type 1 events \(\sim E(dN_1(t) | D
> t)\)
- the terminal event \(\sim E(dN_d(t) | D
> t)\)
and is defined as \(\mu_1(t)=E(N_1^*(t))\) \[\begin{align}
\int_0^t S(u) d R_1(u)
\end{align}\] where \(S(t)=P(D \geq
t)\) and \(dR_1(t) = E(dN_1^*(t) | D
> t)\)
and can therefore be estimated by a
- Kaplan-Meier estimator, \(\hat
S(u)\)
- Nelson-Aalen estimator for \(R_1(t)\)
\[\begin{align}
\hat R_1(t) & = \sum_i \int_0^t \frac{1}{Y_\bullet
(s)} dN_{1i}(s)
\end{align}\] where \(Y_{\bullet}(t)=
\sum_i Y_i(t)\) 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))
#> id: 1
#> entry time status rr rr2 dtime fdeath death start stop x strata
#> 1 0.0 451.1 1 1 1 3291 1 0 0.0 451.1 1.5212 0
#> 201 451.1 2687.9 1 1 1 3291 1 0 451.1 2687.9 0.3290 0
#> 337 2687.9 3290.8 0 1 1 3291 1 1 2687.9 3290.8 -0.4887 0
#> ------------------------------------------------------------
#> id: 7
#> entry time status rr rr2 dtime fdeath death start stop x strata
#> 7 0 658.3 0 1 1 658.3 1 1 0 658.3 -0.04719 0
#> ------------------------------------------------------------
#> id: 9
#> entry time status rr rr2 dtime fdeath death start stop x strata
#> 9 0.0 433.5 1 1 1 505.3 1 0 0.0 433.5 -0.3530 0
#> 205 433.5 505.3 0 1 1 505.3 1 1 433.5 505.3 0.7694 0
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)
bplot(rxr,se=TRUE,robust=TRUE,add=TRUE,col=4)
# 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))
#> times mean se-mean CI-2.5% CI-97.5% strata
#> 1 1000 1.29 0.0989616 1.109913 1.499306 0
#> 2 2000 1.81 0.1381837 1.558450 2.102153 0
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)
bplot(rxr,se=TRUE,robust=TRUE,add=TRUE,col=1:2)
out <- recurrentMarginal(xr,dr)
bplot(out,se=TRUE,ylab="marginal mean",col=1:2)
Further, 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)
bplot(rxr,se=TRUE,robust=TRUE,add=TRUE,col=1:2)
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)
bplot(outX,add=TRUE,col=3)
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)
#>
#> statusD
#> status 0 1 2 3
#> 0 82 0 0 118
#> 1 0 243 0 0
#> 2 0 0 32 0
#>
#> statusD
#> death 0 1 2 3
#> 0 82 243 32 0
#> 1 0 0 0 118
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 muP semuP muPAt semuPAt
#> [1,] 500 0.7296607 0.08934148 0.7249944 0.08905594 0.9968040
#> [2,] 1000 1.0253633 0.11701067 1.0152615 0.11651045 0.9957250
#> [3,] 1500 1.2942861 0.15154426 1.2960853 0.15078170 0.9949681
#> [4,] 2000 1.5821804 0.19166318 1.5505537 0.19135356 0.9983846
#> [5,] 2500 1.7367039 0.20788926 1.6836330 0.20737040 0.9975041
#> [6,] 3000 1.8824299 0.23035386 1.8141174 0.22956165 0.9965609
#> [7,] 3500 2.0447577 0.26163802 1.9734070 0.25940041 0.9914477
#> [8,] 4000 2.2148153 0.30878777 2.1219632 0.30363205 0.9833034
#> [9,] 4500 2.2457349 0.32054957 2.1413394 0.31524586 0.9834543
#> [10,] 5000 2.2457349 0.32054957 2.1413394 0.31524586 0.9834543
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))
#> times muP semuP muPAt semuPAt
#> [1,] 500 0.7296607 0.08934148 0.7237309 0.08900172 0.9961971
#> [2,] 1000 1.0253633 0.11701067 1.0161564 0.11620504 0.9931149
#> [3,] 1500 1.2942861 0.15154426 1.2667503 0.15011439 0.9905647
#> [4,] 2000 1.5821804 0.19166318 1.5082431 0.18777935 0.9797362
#> [5,] 2500 1.7367039 0.20788926 1.6111317 0.20257311 0.9744280
#> [6,] 3000 1.8824299 0.23035386 1.7159830 0.22374266 0.9712998
#> [7,] 3500 2.0447577 0.26163802 1.8292585 0.25298077 0.9669114
#> [8,] 4000 2.2148153 0.30878777 1.9265685 0.29398844 0.9520728
#> [9,] 4500 2.2457349 0.32054957 1.9218574 0.30431456 0.9493526
#> [10,] 5000 2.2457349 0.32054957 1.9218574 0.30431456 0.9493526
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$semuP[k])
lines(rep(t,2)-2,ci2,col=2,lty=1,lwd=2)
lines(rep(t,2)+2,ci1,col=1,lty=1,lwd=2)
k <- k+1
}
legend("bottomright",c("Eff-pred"),lty=1,col=c(1,3))
In the case where covariates might be important but we are still
interested in the marginal mean we can also augment wrt these
covariates
n <- 200
X <- matrix(rbinom(n*2,1,0.5),n,2)
colnames(X) <- paste("X",1:2,sep="")
###
r1 <- exp( X %*% c(0.3,-0.3))
rd <- exp( X %*% c(0.3,-0.3))
rc <- exp( X %*% c(0,0))
fz <- NULL
rr <- mets:::simGLcox(n,base1,ddr,var.z=0,r1=r1,rd=rd,rc=rc,fz,model="twostage",cens=3/5000)
rr <- cbind(rr,X[rr$id+1,])
dtable(rr,~statusD+status+death,level=2,response=1)
#>
#> statusD
#> status 0 1 3
#> 0 93 0 107
#> 1 0 802 0
#>
#> statusD
#> death 0 1 3
#> 0 93 476 0
#> 1 0 326 107
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))
#> muP muPA muPAt semuP semuPA semuPAt
#> [1,] 1.239629 1.226904 1.219113 0.1113222 0.1104686 0.1102564 0.9904262
#> [2,] 2.260753 2.237327 2.218786 0.1888720 0.1856083 0.1850181 0.9795950
#> [3,] 3.380778 3.284093 3.299408 0.3116231 0.2998621 0.2970862 0.9533513
#> [4,] 4.715639 4.652729 4.588619 0.4959756 0.4640347 0.4579825 0.9233974
#> [5,] 5.978661 5.817999 5.773031 0.6718212 0.6155609 0.6038833 0.8988751
#> [6,] 7.029352 6.955265 6.802368 0.8586233 0.7963062 0.7710258 0.8979792
#> [7,] 8.520280 8.217099 8.009947 1.2296894 1.0756621 1.0568620 0.8594544
#> [8,] 9.780973 9.143637 8.813562 1.7113961 1.4189071 1.4040811 0.8204302
#> [9,] 10.871472 10.357563 9.395688 2.1044673 1.7855074 1.6866794 0.8014757
#> [10,] 11.296956 10.735081 9.648354 2.2567311 1.8849404 1.7901175 0.7932347
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=out$se.mu)
#> times mean se-mean CI-2.5% CI-97.5%
#> 1 500 0.8806899 0.06519655 0.7617427 1.018211
#> 2 1000 1.2224722 0.10539353 1.0324110 1.447523
#> 3 1500 1.3702238 0.14414996 1.1149155 1.683996
#> 4 2000 1.4239958 0.17001617 1.1268827 1.799446
#> 5 2500 1.4334297 0.17708838 1.1251633 1.826153
#> 6 3000 1.4352968 0.17907319 1.1239334 1.832917
#> 7 3500 1.4359348 0.17990713 1.1232758 1.835621
#> 8 4000 1.4360308 0.18005391 1.1231443 1.836081
#> 9 4500 1.4360356 0.18006355 1.1231342 1.836110
#> 10 5000 1.4360358 0.18006394 1.1231338 1.836111
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:
n <- 100
X <- matrix(rbinom(n*2,1,0.5),n,2)
colnames(X) <- paste("X",1:2,sep="")
###
r1 <- exp( X %*% c(0.3,-0.3))
rd <- exp( X %*% c(0.3,-0.3))
rc <- exp( X %*% c(0,0))
fz <- NULL
rr <- mets:::simGLcox(n,base1,ddr,var.z=1,r1=r1,rd=rd,rc=rc,fz,cens=1/5000,type=2)
rr <- cbind(rr,X[rr$id+1,])
out <- recreg(Event(start,stop,statusD)~X1+X2+cluster(id),data=rr,cause=1,death.code=3,cens.code=0)
outs <- recreg(Event(start,stop,statusD)~X1+X2+cluster(id),data=rr,cause=1,death.code=3,cens.code=0,
cens.model=~strata(X1,X2))
summary(out)$coef
#> Estimate S.E. dU^-1/2 P-value
#> X1 0.3486562 0.3510510 0.09720215 0.3206230
#> X2 0.3159378 0.3388077 0.09967828 0.3510788
summary(outs)$coef
#> Estimate S.E. dU^-1/2 P-value
#> X1 0.2963320 0.3448975 0.09772802 0.3902364
#> X2 0.2551964 0.3390756 0.10024137 0.4516759
## checking baseline
par(mfrow=c(1,1))
bplot(out)
bplot(outs,add=TRUE,col=2)
lines(scalecumhaz(base1,1),col=3,lwd=2)
We note that for the extended censoring model we gain a little
efficiency and that the estimates are close to the true values.
Also possible to do IPCW regression at fixed time-point
outipcw <- recregIPCW(Event(start,stop,statusD)~X1+X2+cluster(id),data=rr,cause=1,death.code=3,
cens.code=0,times=2000)
outipcws <- recregIPCW(Event(start,stop,statusD)~X1+X2+cluster(id),data=rr,cause=1,death.code=3,
cens.code=0,times=2000,cens.model=~strata(X1,X2))
summary(outipcw)$coef
#> Estimate Std.Err 2.5% 97.5% P-value
#> (Intercept) 0.8376610 0.3071504 0.2356573 1.4396647 0.0063874
#> X1 0.4593771 0.3446980 -0.2162185 1.1349727 0.1826321
#> X2 0.1168024 0.3501243 -0.5694285 0.8030334 0.7386793
summary(outipcws)$coef
#> Estimate Std.Err 2.5% 97.5% P-value
#> (Intercept) 0.90967778 0.3061090 0.3097152 1.5096403 0.002961126
#> X1 0.36067230 0.3348409 -0.2956038 1.0169484 0.281415330
#> X2 0.08605183 0.3376382 -0.5757069 0.7478106 0.798828145
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
#> Estimate S.E. dU^-1/2 P-value
#> X1 0.3308270 0.2973404 0.08977266 0.2658714
#> X2 0.2612694 0.2855146 0.09152510 0.3601484
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)$coef
#> Estimate S.E. dU^-1/2 P-value
#> X1 0.3388982 0.3231606 0.09350977 0.2943166
#> X2 0.3144569 0.3110926 0.09592269 0.3121053
outCW <- recreg(Event(start,stop,statusDC)~X1+X2+cluster(id),data=rr,cause=c(1,3),
death.code=c(3,4),cens.code=0,wcomp=c(1,2))
summary(outCW)$coef
#> Estimate S.E. dU^-1/2 P-value
#> X1 0.3304876 0.3003393 0.09021130 0.2711662
#> X2 0.3132135 0.2885475 0.09256241 0.2777077
bplot(out,ylab="Mean composite")
bplot(outC,col=2,add=TRUE)
bplot(outCW,col=3,add=TRUE)
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,])
#> pred se-log lower upper
#> [1,] 5.823597 0.3817794 2.755625 12.30729
#> [2,] 5.823597 0.3817794 2.755625 12.30729
#> [3,] 5.823597 0.3817794 2.755625 12.30729
#> [4,] 5.823597 0.3817794 2.755625 12.30729
#> [5,] 5.823597 0.3817794 2.755625 12.30729
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)$coef
#> Estimate S.E. dU^-1/2 P-value
#> X1 0.3486562 0.3510510 0.09720215 0.3206230
#> X2 0.3159378 0.3388077 0.09967828 0.3510788
outA <- recreg(Event(start,stop,statusD)~X1+X2+cluster(id),data=rr,cause=1,death.code=3,cens.code=0,
augment.model=~Nt+X1+X2,augment=outi$lindyn.augment)
summary(outA)$coef
#> Estimate S.E. dU^-1/2 P-value
#> X1 0.2907480 0.3407406 0.09686369 0.3935027
#> X2 0.2017009 0.3255009 0.09837819 0.5354796
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.
Two-stage modelling
Above we simulated data with a terminal event on Cox form and
recurrent events satisfying the Ghosh-Lin model.
Now we fit the two-stage model (the recreg must be called with
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)
outs <- phreg(Event(start,stop,statusD==3)~X1+X2+cluster(id),data=rr)
tsout <- twostageREC(outs,out,data=rr)
summary(tsout)
#> Ghosh-Lin(recurrent)-Cox(terminal) mean model
#>
#> 100 clusters
#> coeffients:
#> Estimate Std.Err 2.5% 97.5% P-value
#> dependence1 1.34706 0.16891 1.01600 1.67813 0
#>
#> var,shared:
#> Estimate Std.Err 2.5% 97.5% P-value
#> dependence1 1.34706 0.16891 1.01600 1.67813 0
Standard errors are computed assuming that the parameters of out and
outs are both known, and therefore propobly a bit to small. We could do
a bootstrap to get more reliable standard errors.
Simulations with specific structure
The function simGLcox can simulate data where the recurrent process
has mean on Ghosh-Lin form. The key is that \[\begin{align*}
E(N_1(t) | X) & = \Lambda_0(t) \exp(X^T \beta) = \int_0^t S(t|X,Z)
dR(t|X,Z)
\end{align*}\] where \(Z\) is a
possible frailty. Therefore \[\begin{align*}
R(t|X,Z) & = \frac{Z \Lambda_0(t) \exp(X^T \beta) }{S(t|X,Z)}
\end{align*}\] leads to a Ghosh-Lin model. We can choose the
survival model to have Cox form among survivors by the option
model=“twostage”, otherwise model=“frailty” uses the survival model with
rate \(Z \lambda_d(t) rd\). The \(Z\) is gamma distributed with a variance
that can be specified. The simulations are based on a piecwise-linear
approximation of the hazard functions for \(S(t|X,Z)\) and \(R(t|X,Z)\).
n <- 100
X <- matrix(rbinom(n*2,1,0.5),n,2)
colnames(X) <- paste("X",1:2,sep="")
###
r1 <- exp( X %*% c(0.3,-0.3))
rd <- exp( X %*% c(0.3,-0.3))
rc <- exp( X %*% c(0,0))
rr <- mets:::simGLcox(n,base1,ddr,var.z=0,r1=r1,rd=rd,rc=rc,model="twostage",cens=3/5000)
rr <- cbind(rr,X[rr$id+1,])
We can also simulate from models where the terminal event is on Cox
form and the rate among survivors is on Cox form.
- \(E(dN_1 | D>t, X) = \lambda_1(t)
r_1\)
- \(E(dN_d | D>t, X) = \lambda_d(t)
r_d\)
underlying these models we have a shared frailty model
rr <- mets:::simGLcox(100,base1,ddr,var.z=1,r1=r1,rd=rd,rc=rc,type=3,cens=3/5000)
rr <- cbind(rr,X[rr$id+1,])
margsurv <- phreg(Surv(start,stop,statusD==3)~X1+X2+cluster(id),rr)
recurrent <- phreg(Surv(start,stop,statusD==1)~X1+X2+cluster(id),rr)
estimate(margsurv)
#> Estimate Std.Err 2.5% 97.5% P-value
#> X1 0.1210 0.2601 -0.3887 0.6307 0.641722
#> X2 -0.7358 0.2821 -1.2887 -0.1829 0.009104
estimate(recurrent)
#> Estimate Std.Err 2.5% 97.5% P-value
#> X1 0.10518 0.2758 -0.4354 0.6458 0.7029
#> X2 0.08246 0.2833 -0.4729 0.6378 0.7710
par(mfrow=c(1,2));
plot(margsurv); lines(ddr,col=3);
plot(recurrent); lines(base1,col=3)
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 \(N_1^*(t)^2\) can
be written as \[\begin{align*}
\sum_{k=0}^K \int_0^t I(D > s) I(N_1^*(s-)=k) f(k) dN_1^*(s)
\end{align*}\] with \(f(k)=(k+1)^2 -
k^2\), 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 for \(E( (N_1^*(t))^2 )\)
\[\begin{align}
\hat \mu_{1,2}(t) & = \sum_{k=0}^K k^2 ( \hat F_{k}(t) - \hat
F_{k+1}(t) ).
\end{align}\]
We use the esimator of the probabilty of exceeding “k” events based
on the fact that \(I(N_1^*(t) \geq k)\)
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)
#>
#> status 0 1 2
#> death
#> 0 82 247 27
#> 1 118 0 0
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(oop$times,oop$prob,type="l")
summaryTimeobject(oop$times,oop$prob,se.mu=oop$se.prob,times=1000)
#> times mean se-mean CI-2.5% CI-97.5%
#> N=0 1000 0.552391858 0.03882814 0.481298393 0.63398667
#> exceed>=1 1000 0.447608142 0.03882814 0.377622992 0.53056369
#> exceed>=2 1000 0.233078989 0.03344195 0.175942726 0.30876989
#> exceed>=3 1000 0.139370028 0.02817357 0.093776998 0.20712973
#> exceed>=4 1000 0.088317936 0.02319248 0.052785841 0.14776799
#> exceed>=5 1000 0.044860816 0.01791420 0.020509349 0.09812563
#> exceed>=6 1000 0.015109770 0.01051121 0.003864586 0.05907623
#> exceed>=7 1000 0.008651055 0.00856731 0.001241915 0.06026236
#> exceed>=8 1000 0.000000000 0.00000000 NaN NaN
#> exceed>=9 1000 0.000000000 0.00000000 NaN NaN
#> exceed>=10 1000 0.000000000 0.00000000 NaN NaN
#> exceed>=11 1000 0.000000000 0.00000000 NaN NaN
#> exceed>=12 1000 0.000000000 0.00000000 NaN NaN
#> exceed>=13 1000 0.000000000 0.00000000 NaN NaN
#> exceed>=14 1000 0.000000000 0.00000000 NaN NaN
#> exceed>=15 1000 0.000000000 0.00000000 NaN NaN
#> exceed>=16 1000 0.000000000 0.00000000 NaN NaN
We note from the plot that the estimates are quite similar.
Finally, we make a plot with 95% confidence intervals
matplot(oop$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)
#>
#> status 0 1 2
#> death
#> 0 20 536 85
#> 1 180 0 0
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$strata <- 1
dtable(rr,~death+status)
#>
#> status 0 1 2
#> death
#> 0 20 536 85
#> 1 180 0 0
covrp <- covarianceRecurrent(rr,1,2,status="status",death="death",
start="entry",stop="time",id="id",names.count="Count")
par(mfrow=c(1,3))
plot(covrp)
# with strata, each strata in matrix column, provides basis for fast Bootstrap
covrpS <- covarianceRecurrentS(rr,1,2,status="status",death="death",
start="entry",stop="time",strata="strata",id="id",names.count="Count")
Bootstrap standard errors for terms
First fitting the model again to get our estimates of interst, and
then computing them for some specific time-points
times <- seq(500,5000,500)
coo1 <- covarianceRecurrent(rr,1,2,status="status",start="entry",stop="time")
#
mug <- Cpred(cbind(coo1$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()
#> R version 4.3.2 (2023-10-31)
#> Platform: aarch64-apple-darwin22.6.0 (64-bit)
#> Running under: macOS Sonoma 14.3.1
#>
#> Matrix products: default
#> BLAS: /Users/kkzh/.asdf/installs/R/4.3.2/lib/R/lib/libRblas.dylib
#> LAPACK: /Users/kkzh/.asdf/installs/R/4.3.2/lib/R/lib/libRlapack.dylib; LAPACK version 3.11.0
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> time zone: Europe/Copenhagen
#> tzcode source: internal
#>
#> attached base packages:
#> [1] splines stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] ggplot2_3.4.4 cowplot_1.1.1 mets_1.3.4 timereg_2.0.5 survival_3.5-7
#>
#> loaded via a namespace (and not attached):
#> [1] sass_0.4.7 utf8_1.2.4 future_1.33.1
#> [4] generics_0.1.3 lattice_0.22-5 listenv_0.9.1
#> [7] digest_0.6.34 magrittr_2.0.3 evaluate_0.23
#> [10] grid_4.3.2 mvtnorm_1.2-4 fastmap_1.1.1
#> [13] jsonlite_1.8.8 Matrix_1.6-5 fansi_1.0.6
#> [16] scales_1.2.1 isoband_0.2.7 codetools_0.2-19
#> [19] numDeriv_2016.8-1.1 jquerylib_0.1.4 lava_1.7.4
#> [22] cli_3.6.2 rlang_1.1.3 parallelly_1.37.0
#> [25] future.apply_1.11.1 munsell_0.5.0 withr_3.0.0
#> [28] cachem_1.0.8 yaml_2.3.7 tools_4.3.2
#> [31] parallel_4.3.2 ucminf_1.2.0 dplyr_1.1.3
#> [34] colorspace_2.1-0 globals_0.16.2 vctrs_0.6.5
#> [37] R6_2.5.1 lifecycle_1.0.4 MASS_7.3-60
#> [40] pkgconfig_2.0.3 bslib_0.5.1 pillar_1.9.0
#> [43] gtable_0.3.4 glue_1.7.0 Rcpp_1.0.12
#> [46] tidyselect_1.2.0 xfun_0.41 tibble_3.2.1
#> [49] highr_0.10 knitr_1.45 farver_2.1.1
#> [52] htmltools_0.5.6.1 labeling_0.4.3 rmarkdown_2.25
#> [55] compiler_4.3.2
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.