# surv.km: Estimates survival using Kaplan-Meier estimation In landest: Landmark Estimation of Survival and Treatment Effect

## Description

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

## Usage

 1 2 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

## 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

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

 1 2 3 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)  ### Example output $S.estimate
[1] 0.4439353


landest documentation built on May 30, 2017, 1:24 a.m.