easy.twostage: Fits two-stage model for describing depdendence in survival...

In mets: Analysis of Multivariate Event Times

Description

If clusters contain more than two times, the algoritm uses a composite likelihood based on the pairwise bivariate models.

Usage

easy.twostage(margsurv = NULL, data = sys.parent(),
  score.method = "nlminb", status = "status", time = "time",
  entry = NULL, id = "id", Nit = 60, detail = 0, silent = 1,
  weights = NULL, control = list(), theta = NULL, theta.formula = NULL,
  desnames = NULL, deshelp = 0, var.link = 1, iid = 1, step = 0.5,
  model = "plackett", marginal.surv = NULL, strata = NULL,
  max.clust = NULL, se.clusters = NULL)

Arguments

margsurv	model
data	data frame
score.method	Scoring method
status	Status at exit time
time	Exit time
entry	Entry time
id	name of cluster variable in data frame
Nit	Number of iterations
detail	Detail for more output for iterations
silent	Debug information
weights	Weights for log-likelihood, can be used for each type of outcome in 2x2 tables.
control	Optimization arguments
theta	Starting values for variance components
theta.formula	design for depedence, either formula or design function
desnames	names for dependence parameters
deshelp	if 1 then prints out some data sets that are used, on on which the design function operates
var.link	Link function for variance (exp link)
iid	Calculate i.i.d. decomposition
step	Step size for newton-raphson
model	plackett or clayton-oakes model
marginal.surv	vector of marginal survival probabilities
strata	strata for fitting
max.clust	max clusters
se.clusters	clusters for iid decomposition for roubst standard errors

Details

The reported standard errors are based on the estimated information from the likelihood assuming that the marginals are known.

Examples

data(prt)
margp<- coxph(Surv(time,status==1)~factor(country),data=prt)
fitco<-twostage(margp,data=prt,clusters=prt$id)
summary(fitco)

des <- model.matrix(~-1+factor(zyg),data=prt);
fitco<-twostage(margp,data=prt,theta.des=des,clusters=prt$id)
summary(fitco)

dfam <- simSurvFam(1000)
dfam <- fast.reshape(dfam,var=c("x","time","status"))

desfs <- function(x,num1="num1",num2="num2")
{
pp <- (x[num1]=="m")*(x[num2]=="f")*1   ## mother-father
pc <- (x[num1]=="m" | x[num1]=="f")*(x[num2]=="b1" | x[num2]=="b2")*1 ## mother-child
cc <- (x[num1]=="b1")*(x[num2]=="b1" | x[num2]=="b2")*1               ## child-child
c(pp,pc,cc)
}

marg <- coxph(Surv(time,status)~factor(num),data=dfam)
out3 <- easy.twostage(marg,data=dfam,time="time",status="status",id="id",deshelp=0,
                      score.method="fisher.scoring",theta.formula=desfs,
                      desnames=c("parent-parent","parent-child","child-cild"))
summary(out3)

mets documentation built on May 2, 2019, 4:43 p.m.