get.pointEst {causalCmprsk}

Returns point estimates and conf.level% confidence intervals corresponding to a specific time point

Description

The confidence interval returned by this function corresponds to the value conf.level passed to the function fit.cox or fit.nonpar. The first input argument cmprsk.obj is a result corresponding to conf.level.

Usage

get.pointEst(cmprsk.obj, timepoint)

Arguments

cmprsk.obj

a cmprsk object returned by one of the functions fit.cox or fit.nonpar

timepoint

a scalar value of the time point of interest

Value

A list with the following fields:

time
a scalar timepoint passed into the function
trt.0
a list of estimates of the absolute counterfactual parameters corresponding to A=0 and the type of event E. trt.0 has the number of fields as the number of different types of events in the data set. For each type of event there is a list of estimates:

• CumHaz a point estimate of the cumulative hazard

• CumHaz.CI.L a bootstrap-based quantile estimate of a lower bound of a conf.level% confidence interval for the cumulative hazard

• CumHaz.CI.U a bootstrap-based quantile estimate of an upper bound of a conf.level% confidence interval for the cumulative hazard

• CIF a point estimate of the cumulative incidence function

• CIF.CI.L a bootstrap-based quantile estimate of a lower bound of a conf.level% confidence interval for the cumulative incidence function

• CIF.CI.U a bootstrap-based quantile estimate of an upper bound of a conf.level% confidence interval for the cumulative incidence function

• RMT a point estimate of the restricted mean time

• RMT.CI.L a bootstrap-based quantile estimate of a lower bound of a conf.level% confidence interval for the restricted mean time

• RMT.CI.U a bootstrap-based quantile estimate of an upper bound of a conf.level% confidence interval for the restricted mean time

trt.1
a list of estimates of the absolute counterfactual parameters corresponding to A=1 and the type of event E. trt.1 has the number of fields as the number of different types of events in the data set. For each type of event there is a list of estimates:

• CumHaz a point estimate of the cumulative hazard

• CumHaz.CI.L a bootstrap-based quantile estimate of a lower bound of a conf.level% confidence interval for the cumulative hazard

• CumHaz.CI.U a bootstrap-based quantile estimate of an upper bound of a conf.level% confidence interval for the cumulative hazard

• CIF a point estimate of the cumulative incidence function

• CIF.CI.L a bootstrap-based quantile estimate of a lower bound of a conf.level% confidence interval for the cumulative incidence function

• CIF.CI.U a bootstrap-based quantile estimate of an upper bound of a conf.level% confidence interval for the cumulative incidence function

• RMT a point estimate of the restricted mean time

• RMT.CI.L a bootstrap-based quantile estimate of a lower bound of a conf.level% confidence interval for the restricted mean time

• RMT.CI.U a bootstrap-based quantile estimate of an upper bound of a conf.level% confidence interval for the restricted mean time

trt.eff
a list of estimates of the treatment effect measures corresponding to the type of event E. trt.eff has the number of fields as the number of different types of events in the data set. For each type of event there is a list of estimates:

• log.CumHazR a point estimate of the log of the ratio of hazards between two treatment arms

• log.CumHazR.CI.L a bootstrap-based quantile estimate of a lower bound of a conf.level% confidence interval for the log of the ratio of hazards between two treatment arms

• log.CumHazR.CI.U a bootstrap-based quantile estimate of an upper bound of a conf.level% confidence interval for the log of the ratio of hazards between two treatment arms

• RD a point estimate of the risk difference between two treatment arms

• RD.CI.L a bootstrap-based quantile estimate of a lower bound of a conf.level% confidence interval for the risk difference between two treatment arms

• RD.CI.U a bootstrap-based quantile estimate of an upper bound of a conf.level% confidence interval for the risk difference between two treatment arms

• RR a point estimate of the risk ratio between two treatment arms

• RR.CI.L a bootstrap-based quantile estimate of a lower bound of a conf.level% confidence interval for the risk ratio between two treatment arms

• RR.CI.U a bootstrap-based quantile estimate of an upper bound of a conf.level% confidence interval for the risk ratio between two treatment arms

• ATE.RMT a point estimate of the restricted mean time difference between two treatment arms

• ATE.RMT.CI.L a bootstrap-based quantile estimate of a lower bound of a conf.level% confidence interval for the restricted mean time difference between two treatment arms

• ATE.RMT.CI.U a bootstrap-based quantile estimate of an upper bound of a conf.level% confidence interval for the restricted mean time difference between two treatment arms

See Also

fit.cox, fit.nonpar, causalCmprsk

Examples

# create a data set
n <- 1000
set.seed(7)
c1 <-  runif(n)
c2 <- as.numeric(runif(n)< 0.2)
set.seed(77)
cf.m.T1 <- rweibull(n, shape=1, scale=exp(-(-1 + 2*c1)))
cf.m.T2 <-  rweibull(n, shape=1, scale=exp(-(1 + 1*c2)))
cf.m.T <- pmin( cf.m.T1, cf.m.T2)
cf.m.E <- rep(0, n)
cf.m.E[cf.m.T1<=cf.m.T2] <- 1
cf.m.E[cf.m.T2<cf.m.T1] <- 2
set.seed(77)
cf.s.T1 <- rweibull(n, shape=1, scale=exp(-1*c1 ))
cf.s.T2 <-  rweibull(n, shape=1, scale=exp(-2*c2))
cf.s.T <- pmin( cf.s.T1, cf.s.T2)
cf.s.E <- rep(0, n)
cf.s.E[cf.s.T1<=cf.s.T2] <- 1
cf.s.E[cf.s.T2<cf.s.T1] <- 2
exp.z <- exp(0.5 + 1*c1 - 1*c2)
pr <- exp.z/(1+exp.z)
TRT <- ifelse(runif(n)< pr, 1, 0)
X <- ifelse(TRT==1, cf.m.T, cf.s.T)
E <- ifelse(TRT==1, cf.m.E, cf.s.E)
covs.names <- c("c1", "c2")
data <- data.frame(X=X, E=E, TRT=TRT, c1=c1, c2=c2)
form.txt <- paste0("TRT", " ~ ", paste0(c("c1", "c2"), collapse = "+"))
trt.formula <- as.formula(form.txt)
wei <- get.weights(formula=trt.formula, data=data, wtype = "overlap")
hist(wei$ps[data$TRT==1], col="red", breaks = seq(0,1,0.05))
par(new=TRUE)
hist(wei$ps[data$TRT==0], col="blue", breaks = seq(0,1,0.05))
# Nonparametric estimation:
res.ATE <- fit.nonpar(df=data, X="X", E="E", trt.formula=trt.formula, wtype="stab.ATE")
nonpar.pe <- get.pointEst(res.ATE, 0.5)
nonpar.pe$trt.eff[[1]]$RD
# Cox-based estimation:
res.cox.ATE <- fit.cox(df=data, X="X", E="E", trt.formula=trt.formula, wtype="stab.ATE")
cox.pe <- get.pointEst(res.cox.ATE, 0.5)
cox.pe$trt.eff[[1]]$RD

# please see our package vignette for practical examples