Likelihood ratio test for covariates — test

This function fits separate models for each distinct value of the factor covariate and computes a likelihood ratio test to test whether there are significant differences between groups.

Usage

test_elife(
  time,
  time2 = NULL,
  event = NULL,
  covariate,
  thresh = 0,
  ltrunc = NULL,
  rtrunc = NULL,
  type = c("right", "left", "interval", "interval2"),
  family = c("exp", "gp", "weibull", "gomp", "gompmake", "extgp", "extweibull", "perks",
    "perksmake", "beard", "beardmake"),
  weights = rep(1, length(time)),
  arguments = NULL,
  ...
)

Arguments

time: excess time of the event of follow-up time, depending on the value of event
time2: ending excess time of the interval for interval censored data only.
event: status indicator, normally 0=alive, 1=dead. Other choices are TRUE/FALSE (TRUE for death). For interval censored data, the status indicator is 0=right censored, 1=event at time, 2=left censored, 3=interval censored. Although unusual, the event indicator can be omitted, in which case all subjects are assumed to have experienced an event.
covariate: vector of factors, logical or integer whose distinct values define groups
thresh: vector of thresholds
ltrunc: lower truncation limit, default to NULL
rtrunc: upper truncation limit, default to NULL
type: character string specifying the type of censoring. Possible values are "right", "left", "interval", "interval2".
family: string; choice of parametric family
weights: weights for observations
arguments: a named list specifying default arguments of the function that are common to all elife calls
...: additional arguments for optimization, currently ignored.

Value

a list with elements

stat likelihood ratio statistic
df degrees of freedom
pval the p-value obtained from the asymptotic chi-square approximation.

Examples

test <- with(subset(dutch, ndays > 39082),
 test_elife(
 time = ndays,
 thresh = 39082L,
 covariate = gender,
 ltrunc = ltrunc,
 rtrunc = rtrunc,
 family = "exp"))
 test
#> Model: exponential distribution. 
#> Threshold: 39082 
#> Number of exceedances per covariate level:
#>   male female 
#>     42    205 
#> 
#> Likelihood ratio statistic: 2.78
#> Null distribution: chi-square (1)
#> Asymptotic p-value:  0.0954