get.pointEst {causalCmprsk}  R Documentation 
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 
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 hazardCumHaz.CI.L
a bootstrapbased quantile estimate of a lower bound of aconf.level
% confidence interval for the cumulative hazardCumHaz.CI.U
a bootstrapbased quantile estimate of an upper bound of aconf.level
% confidence interval for the cumulative hazardCIF
a point estimate of the cumulative incidence functionCIF.CI.L
a bootstrapbased quantile estimate of a lower bound of aconf.level
% confidence interval for the cumulative incidence functionCIF.CI.U
a bootstrapbased quantile estimate of an upper bound of aconf.level
% confidence interval for the cumulative incidence functionRMT
a point estimate of the restricted mean timeRMT.CI.L
a bootstrapbased quantile estimate of a lower bound of aconf.level
% confidence interval for the restricted mean timeRMT.CI.U
a bootstrapbased quantile estimate of an upper bound of aconf.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 hazardCumHaz.CI.L
a bootstrapbased quantile estimate of a lower bound of aconf.level
% confidence interval for the cumulative hazardCumHaz.CI.U
a bootstrapbased quantile estimate of an upper bound of aconf.level
% confidence interval for the cumulative hazardCIF
a point estimate of the cumulative incidence functionCIF.CI.L
a bootstrapbased quantile estimate of a lower bound of aconf.level
% confidence interval for the cumulative incidence functionCIF.CI.U
a bootstrapbased quantile estimate of an upper bound of aconf.level
% confidence interval for the cumulative incidence functionRMT
a point estimate of the restricted mean timeRMT.CI.L
a bootstrapbased quantile estimate of a lower bound of aconf.level
% confidence interval for the restricted mean timeRMT.CI.U
a bootstrapbased quantile estimate of an upper bound of aconf.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 armslog.CumHazR.CI.L
a bootstrapbased quantile estimate of a lower bound of aconf.level
% confidence interval for the log of the ratio of hazards between two treatment armslog.CumHazR.CI.U
a bootstrapbased quantile estimate of an upper bound of aconf.level
% confidence interval for the log of the ratio of hazards between two treatment armsRD
a point estimate of the risk difference between two treatment armsRD.CI.L
a bootstrapbased quantile estimate of a lower bound of aconf.level
% confidence interval for the risk difference between two treatment armsRD.CI.U
a bootstrapbased quantile estimate of an upper bound of aconf.level
% confidence interval for the risk difference between two treatment armsRR
a point estimate of the risk ratio between two treatment armsRR.CI.L
a bootstrapbased quantile estimate of a lower bound of aconf.level
% confidence interval for the risk ratio between two treatment armsRR.CI.U
a bootstrapbased quantile estimate of an upper bound of aconf.level
% confidence interval for the risk ratio between two treatment armsATE.RMT
a point estimate of the restricted mean time difference between two treatment armsATE.RMT.CI.L
a bootstrapbased quantile estimate of a lower bound of aconf.level
% confidence interval for the restricted mean time difference between two treatment armsATE.RMT.CI.U
a bootstrapbased quantile estimate of an upper bound of aconf.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
# Coxbased 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