# surv.iptw.km: Estimates survival using inverse probability of treatment... In landest: Landmark Estimation of Survival and Treatment Effect

## Description

Estimates the probability of survival past some specified time using inverse probability of treatment weighted (IPTW) Kaplan-Meier estimation

## Usage

 1 2 surv.iptw.km(tl, dl, tt, var = FALSE, conf.int = FALSE, ps.weights, weight.perturb = NULL,perturb.ps = FALSE, 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.iptw.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

Xie, J., & Liu, C. (2005). Adjusted Kaplan-Meier estimator and log-rank test with inverse probability of treatment weighting for survival data. Statistics in Medicine, 24(20), 3089-3110.

## Examples

 1 2 3 4 5 data(example_obs) W.weight = ps.wgt.fun(treat = example_obs$treat, cov.for.ps = as.matrix(example_obs$Z)) example_obs.treat = example_obs[example_obs$treat == 1,] surv.iptw.km(tl=example_obs.treat$TL, dl = example_obs.treat$DL, tt=2, ps.weights = W.weight[example_obs$treat == 1]) 

### Example output

\$S.estimate
[1] 0.4781018


landest documentation built on May 2, 2019, 10:12 a.m.