random.cif: Random effects model for competing risks data

In mets: Analysis of Multivariate Event Times

Random effects model for competing risks data

Description

Fits a random effects model describing the dependence in the cumulative incidence curves for subjects within a cluster. Given the gamma distributed random effects it is assumed that the cumulative incidence curves are indpendent, and that the marginal cumulative incidence curves are on the form

P(T \leq t, cause=1 | x,z) = P_1(t,x,z) = 1- exp( -x^T A(t) exp(z^T \beta))

We allow a regression structure for the random effects variances that may depend on cluster covariates.

Usage

random.cif(
  cif,
  data,
  cause = NULL,
  cif2 = NULL,
  cause1 = 1,
  cause2 = 1,
  cens.code = NULL,
  cens.model = "KM",
  Nit = 40,
  detail = 0,
  clusters = NULL,
  theta = NULL,
  theta.des = NULL,
  sym = 1,
  step = 1,
  same.cens = FALSE,
  var.link = 0,
  score.method = "nr",
  entry = NULL,
  trunkp = 1,
  ...
)

Arguments

cif	a model object from the comp.risk function with the marginal cumulative incidence of cause2, i.e., the event that is conditioned on, and whose odds the comparision is made with respect to
data	a data.frame with the variables.
cause	specifies the causes related to the death times, the value cens.code is the censoring value.
cif2	specificies model for cause2 if different from cause1.
cause1	cause of first coordinate.
cause2	cause of second coordinate.
cens.code	specificies the code for the censoring if NULL then uses the one from the marginal cif model.
cens.model	specified which model to use for the ICPW, KM is Kaplan-Meier alternatively it may be "cox"
Nit	number of iterations for Newton-Raphson algorithm.
detail	if 0 no details are printed during iterations, if 1 details are given.
clusters	specifies the cluster structure.
theta	specifies starting values for the cross-odds-ratio parameters of the model.
theta.des	specifies a regression design for the cross-odds-ratio parameters.
sym	1 for symmetry 0 otherwise
step	specifies the step size for the Newton-Raphson algorith.m
same.cens	if true then censoring within clusters are assumed to be the same variable, default is independent censoring.
var.link	if var.link=1 then var is on log-scale.
score.method	default uses "nlminb" optimzer, alternatively, use the "nr" algorithm.
entry	entry-age in case of delayed entry. Then two causes must be given.
trunkp	gives probability of survival for delayed entry, and related to entry-ages given above.
...	extra arguments.

Value

returns an object of type 'cor'. With the following arguments:

theta	estimate of proportional odds parameters of model.
var.theta	variance for gamma.
hess	the derivative of the used score.
score	scores at final stage.
score	scores at final stage.
theta.iid	matrix of iid decomposition of parametric effects.

Author(s)

Thomas Scheike

References

A Semiparametric Random Effects Model for Multivariate Competing Risks Data, Scheike, Zhang, Sun, Jensen (2010), Biometrika.

Cross odds ratio Modelling of dependence for Multivariate Competing Risks Data, Scheike and Sun (2012), work in progress.

Examples

 ## Reduce Ex.Timings
 d <- simnordic.random(5000,delayed=TRUE,cordz=0.5,cormz=2,lam0=0.3,country=TRUE)
 times <- seq(50,90,by=10)
 add1 <- timereg::comp.risk(Event(time,cause)~-1+factor(country)+cluster(id),data=d,
 times=times,cause=1,max.clust=NULL)

 ### making group indidcator 
 mm <- model.matrix(~-1+factor(zyg),d)

 out1<-random.cif(add1,data=d,cause1=1,cause2=1,theta=1,same.cens=TRUE)
 summary(out1)

 out2<-random.cif(add1,data=d,cause1=1,cause2=1,theta=1,
		   theta.des=mm,same.cens=TRUE)
 summary(out2)

#########################################
##### 2 different causes
#########################################

 add2 <- timereg::comp.risk(Event(time,cause)~-1+factor(country)+cluster(id),data=d,
                  times=times,cause=2,max.clust=NULL)
 out3 <- random.cif(add1,data=d,cause1=1,cause2=2,cif2=add2,sym=1,same.cens=TRUE)
 summary(out3) ## negative dependence

 out4 <- random.cif(add1,data=d,cause1=1,cause2=2,cif2=add2,theta.des=mm,sym=1,same.cens=TRUE)
 summary(out4) ## negative dependence

mets documentation built on May 29, 2024, 3:51 a.m.