landest (version 1.2)

surv.land.obs: Estimates survival using landmark estimation

Description

Estimates the probability of survival past some specified time using landmark estimation for an observational study setting

Usage

surv.land.obs(tl, dl, tt, landmark, short = NULL, z.cov = NULL, var = FALSE, 
conf.int = FALSE, ps.weights, weight.perturb = NULL, perturb.ps = FALSE, 
perturb.vector = FALSE, bw = NULL)

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

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

landmark

the landmark time

short

a matrix of intermediate event information, there should be two columns for each intermediate event, the first column contains the observed intermediate event time, equal to min(TS, C) where TS is the event time and C is the censoring time, and the second column contains the event indicator, equal to I(TS<C)

z.cov

matrix of baseline covariate information

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.

bw

bandwidth used for kernel estimation, default is NULL

Author

Layla Parast

Details

See documentation for delta.land.obs for details.

References

Parast, L. & Griffin B.A. (2017). Landmark Estimation of Survival and Treatment Effects in Observational Studies. Lifetime Data Analysis, 23:161-182.

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$treat == 1,]
#executable but takes time
#surv.land.obs(tl=example_obs.treat$TL, dl = example_obs.treat$DL, tt=2, landmark = 1, 
#short = cbind(example_obs.treat$TS,example_obs.treat$DS), z.cov = example_obs.treat$Z, 
#ps.weights = W.weight[example_obs$treat == 1])