interval.logitsurv.discrete {mets}

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

This is thus also the cumulative odds model, since

P(T \leq t | x) = \frac{G(t) \exp(x \beta) }{1 + G(t) exp( x \beta) }

The baseline G(t) is written as cumsum(exp(\alpha)) and this is not the standard parametrization that takes log of G(t) as the parameters.

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)

Author(s)

Thomas Scheike

Examples

data(ttpd) 
dtable(ttpd,~entry+time2)
out <- interval.logitsurv.discrete(Interval(entry,time2)~X1+X2+X3+X4,ttpd)
summary(out)

pred <- predictlogitSurvd(out,se=FALSE)
plotSurvd(pred)