mets (version 1.2.8.1)

interval.logitsurv.discrete: Discrete time to event interval censored data

Description

$$ logit(P(T >t | x)) = log(G(t)) + x \beta $$ $$ P(T >t | x) = \frac{1}{1 + G(t) exp( x \beta) } $$

Usage

interval.logitsurv.discrete(
  formula,
  data,
  beta = NULL,
  no.opt = FALSE,
  method = "NR",
  stderr = TRUE,
  weights = NULL,
  offsets = NULL,
  exp.link = 1,
  increment = 1,
  ...
)

Arguments

formula

formula

data

data

beta

starting values

no.opt

optimization TRUE/FALSE

method

NR, nlm

stderr

to return only estimate

weights

weights following id for GLM

offsets

following id for GLM

exp.link

parametrize increments exp(alpha) > 0

increment

using increments dG(t)=exp(alpha) as parameters

...

Additional arguments to lower level funtions lava::NR optimizer or nlm

Details

Input are intervals given by ]t_l,t_r] where t_r can be infinity for right-censored intervals When truly discrete ]0,1] will be an observation at 1, and ]j,j+1] will be an observation at j+1

Likelihood is maximized: $$ \prod P(T_i >t_{il} | x) - P(T_i> t_{ir}| x) $$

Examples

# NOT RUN {
data(ttpd) 
dtable(ttpd,~entry+time2)
out <- interval.logitsurv.discrete(Interval(entry,time2)~X1+X2+X3+X4,ttpd)
summary(out)

n <- 100
Z <- matrix(rbinom(n*4,1,0.5),n,4)
outsim <- simlogitSurvd(out$coef,Z)
outsim <- transform(outsim,left=time,right=time+1)
outsim <- dtransform(outsim,right=Inf,status==0)

outss <- interval.logitsurv.discrete(Interval(left,right)~+X1+X2+X3+X4,outsim)

Z <- matrix(0,5,4)
Z[2:5,1:4] <- diag(4)
pred <- predictlogitSurvd(out,se=FALSE)
plotSurvd(pred)

## simulations 
n <- 100
Z <- matrix(rbinom(n*4,1,0.5),n,4)
outsim <- simlogitSurvd(out$coef,Z)
###
outsim <- transform(outsim,left=time,right=time+1)
outsim <- dtransform(outsim,right=Inf,status==0)

out$coef
outss <- interval.logitsurv.discrete(Interval(left,right)~+X1+X2+X3+X4,outsim)
summary(outss)

# }