surv.iptw.km: Estimates survival using inverse probability of treatment...

In landest: Landmark Estimation of Survival and Treatment Effect

Estimates survival using inverse probability of treatment weighted (IPTW) Kaplan-Meier estimation

Description

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

Usage

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<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.
ps.weights	propensity score (or inverse probability of treatment) weights
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.ps	TRUE or FALSE indicating whether the weight.perturb matrix includes the perturbed propensity score (or inverse probability of treatment) 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.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

Author(s)

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

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]) 

landest documentation built on Aug. 26, 2023, 1:08 a.m.