cox.aalen {timereg}R Documentation

Fit Cox-Aalen survival model

Description

Fits an Cox-Aalen survival model. Time dependent variables and counting process data (multiple events per subject) are possible.

λ_{i}(t) = Y_i(t) ( X_{i}^T(t) α(t) ) \exp(Z_{i}^T β )

The model thus contains the Cox's regression model as special case.

Resampling is used for computing p-values for tests of time-varying effects. Test for proportionality is considered by considering the score processes for the proportional effects of model.

The modelling formula uses the standard survival modelling given in the survival package.

Usage

cox.aalen(formula=formula(data),data=sys.parent(),beta=NULL,Nit=10,detail=0,
start.time=0,max.time=NULL,id=NULL,clusters=NULL,n.sim=500,residuals=0,
robust=1,weighted.test=0,covariance=0,resample.iid=0,weights=NULL,rate.sim=1,
beta.fixed=0,max.clust=1000,exact.deriv=1,silent=0,max.timepoint.sim=100)

Arguments

formula

a formula object with the response on the left of a '~' operator, and the independent terms on the right as regressors. The response must be a survival object as returned by the ‘Surv’ function. Terms with a proportional effect are specified by the wrapper prop(), and cluster variables (for computing robust variances) by the wrapper cluster().

data

a data.frame with the variables.

start.time

start of observation period where estimates are computed.

max.time

end of observation period where estimates are computed. Estimates thus computed from [start.time, max.time]. Default is max of data.

robust

to compute robust variances and construct processes for resampling. May be set to 0 to save memory.

id

For timevarying covariates the variable must associate each record with the id of a subject.

clusters

cluster variable for computation of robust variances.

n.sim

number of simulations in resampling.

weighted.test

to compute a variance weighted version of the test-processes used for testing time-varying effects.

residuals

to returns residuals that can be used for model validation in the function cum.residuals. Estimated martingale increments (dM) and corresponding time vector (time).

covariance

to compute covariance estimates for nonparametric terms rather than just the variances.

resample.iid

to return i.i.d. representation for nonparametric and parametric terms.

beta

starting value for relative risk estimates.

Nit

number of iterations for Newton-Raphson algorithm.

detail

if 0 no details is printed during iterations, if 1 details are given.

weights

weights for weighted analysis.

rate.sim

rate.sim=1 such that resampling of residuals is based on estimated martingales and thus valid in rate case, rate.sim=0 means that resampling is based on counting processes and thus only valid in intensity case.

beta.fixed

option for computing score process for fixed relative risk parameter

max.clust

sets the total number of i.i.d. terms in i.i.d. decompostition. This can limit the amount of memory used by coarsening the clusters. When NULL then all clusters are used. Default is 1000 to save memory and time.

exact.deriv

if 1 then uses exact derivative in last iteration, if 2 then uses exact derivate for all iterations, and if 0 then uses approximation for all computations and there may be a small bias in the variance estimates. For Cox model always exact and all options give same results.

silent

if 1 then opppresses some output.

max.timepoint.sim

considers only this resolution on the time scale for simulations, see time.sim.resolution argument

Details

The data for a subject is presented as multiple rows or 'observations', each of which applies to an interval of observation (start, stop]. For counting process data with the )start,stop] notation is used the 'id' variable is needed to identify the records for each subject. The program assumes that there are no ties, and if such are present random noise is added to break the ties.

Value

returns an object of type "cox.aalen". With the following arguments:

cum

cumulative timevarying regression coefficient estimates are computed within the estimation interval.

var.cum

the martingale based pointwise variance estimates.

robvar.cum

robust pointwise variances estimates.

gamma

estimate of parametric components of model.

var.gamma

variance for gamma.

robvar.gamma

robust variance for gamma.

residuals

list with residuals.

obs.testBeq0

observed absolute value of supremum of cumulative components scaled with the variance.

pval.testBeq0

p-value for covariate effects based on supremum test.

sim.testBeq0

resampled supremum values.

obs.testBeqC

observed absolute value of supremum of difference between observed cumulative process and estimate under null of constant effect.

pval.testBeqC

p-value based on resampling.

sim.testBeqC

resampled supremum values.

obs.testBeqC.is

observed integrated squared differences between observed cumulative and estimate under null of constant effect.

pval.testBeqC.is

p-value based on resampling.

sim.testBeqC.is

resampled supremum values.

conf.band

resampling based constant to construct robust 95% uniform confidence bands.

test.procBeqC

observed test-process of difference between observed cumulative process and estimate under null of constant effect over time.

sim.test.procBeqC

list of 50 random realizations of test-processes under null based on resampling.

covariance

covariances for nonparametric terms of model.

B.iid

Resample processes for nonparametric terms of model.

gamma.iid

Resample processes for parametric terms of model.

loglike

approximate log-likelihood for model, similar to Cox's partial likelihood. Only computed when robust=1.

D2linv

inverse of the derivative of the score function.

score

value of score for final estimates.

test.procProp

observed score process for proportional part of model.

var.score

variance of score process (optional variation estimator for beta.fixed=1 and robust estimator otherwise).

pval.Prop

p-value based on resampling.

sim.supProp

re-sampled absolute supremum values.

sim.test.procProp

list of 50 random realizations of test-processes for proportionality under the model based on resampling.

Author(s)

Thomas Scheike

References

Martinussen and Scheike, Dynamic Regression Models for Survival Data, Springer (2006).

Examples

library(timereg)
data(sTRACE)
# Fits Cox model 
out<-cox.aalen(Surv(time,status==9)~prop(age)+prop(sex)+
prop(vf)+prop(chf)+prop(diabetes),data=sTRACE,max.time=7,n.sim=100)


# makes Lin, Wei, Ying test for proportionality
summary(out)
par(mfrow=c(2,3))
plot(out,score=1) 

# Fits Cox-Aalen model 
out<-cox.aalen(Surv(time,status==9)~prop(age)+prop(sex)+
vf+chf+prop(diabetes),data=sTRACE,max.time=7,n.sim=100)

summary(out)
par(mfrow=c(2,3))
plot(out)

# Fits model Cox-Aalen model  and 
# simulates based on time-grouping to save computing time 
out<- cox.aalen(Surv(time,status==9)~prop(sex)+prop(age)+prop(wmi),data=sTRACE,
        	max.timepoint.sim=30,n.sim=100)
summary(out)
par(mfrow=c(2,2))
plot(out)
plot(out,score=1)

[Package timereg version 1.6-7 Index]