surv.km {landest}

Estimates survival using Kaplan-Meier estimation

Description

Estimates the probability of survival past some specified time using Kaplan-Meier estimation

Usage

surv.km(tl, dl, tt, var = FALSE, conf.int = FALSE, weight.perturb = NULL, 
perturb.vector = FALSE)

Arguments

tl

observed event time of primary outcome, equal to min(T, C) where T is the event time and C is the censoring time.

dl

event indicator, equal to I(T<C) where T is the event time and C is the censoring time.

tt

the time of interest, function estimates the probability of survival past this time

var

TRUE or FALSE; indicates whether a variance estimate for survival is requested, default is FALSE.

conf.int

TRUE or FALSE; indicates whether a 95% confidence interval for survival is requested, default is FALSE.

weight.perturb

a n by x matrix of weights where n = length of tl; used for perturbation-resampling, default is null. If var or conf.int is TRUE and weight.perturb is not provided, the function generates exponential(1) weights.

perturb.vector

TRUE or FALSE; indicates whether a vector of the perturbed values of the survival estimate is requested, default is FALSE. This argument is ignored if both var and conf.int are FALSE.

Details

See documentation for delta.km for details.

Value

A list is returned:

S.estimate

the estimate of survival at the time of interest, \hat{S}(t) = P(T>t)

S.var

the variance estimate of \hat{S}(t); if var = TRUE or conf.int = TRUE

conf.int.normal.S

a vector of size 2; the 95% confidence interval for \hat{S}(t) based on a normal approximation; if conf.int = TRUE

conf.int.quantile.S

a vector of size 2; the 95% confidence interval for \hat{S}(t) based on sample quantiles of the perturbed values, described above; if conf.int = TRUE

perturb.vector

a vector of size x where x is the number of columns of the provided weight.perturb matrix (or x=500 if weight.perturb is not provided); the perturbed values of \hat{S}(t); if perturb.vector = TRUE and either var=TRUE or conf.int = TRUE

Author(s)

Layla Parast

References

Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457-481.

Examples

data(example_rct)
example_rct.treat = example_rct[example_rct$treat == 1,]
surv.km(tl=example_rct.treat$TL, dl = example_rct.treat$DL, tt=2)