mets (version 1.3.4)

random.cif: 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,
  ...
)

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.

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.

Author

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