IPWE_Qopt_DepCen_general: Estimate Quantile-optimal Treatment Regime for...

In QTOCen: Quantile-Optimal Treatment Regimes with Censored Data

Description

This function estimates the Quantile-optimal Treatment Regime for a given quantile level of interest under the assumption that the distribution of censoring time is independent of the set of potential survival times given a set of baseline covariates and treatment actually received.

More specifically, we do stratification by treatment first and then used kernel smoothing to estimate local survival function of censoring time for each treatment group.

Usage

IPWE_Qopt_DepCen_general(data, regimeClass, tau, Domains = NULL, bw,
  moPropen = "BinaryRandom", DepCens = NULL, UseTrueG = FALSE,
  trueG_value = NULL, cluster = FALSE, p_level = 1, s.tol = 1e-05,
  it.num = 8, pop.size = 5000)

Arguments

data	raw data.frame
regimeClass	the class of treatment regimes. e.g., 'txname ~ x1+x2'.
tau	the quantile of interest
Domains	default is NULL.
bw	the bandwidth of local KM model (e.g. see Wang-wang 2008)
moPropen	an optional string for the working model of treatment assignment
DepCens	an optional vector of baseline variable names that the censoring variable depends on. Note that the treatment variable is always treated as dependent with the censoring time. If unspecified (DepCens=NULL), then all variables on the right side of regimeClass are used for DepCens
UseTrueG	logical. Whether the true survival probability of each patient is provided.
trueG_value	default is NULL. IF UseTrueG=FALSE, trueG_value should be NULL.
cluster	default is FALSE. This can also be an object of the 'cluster' class returned by one of the makeCluster commands in the parallel package or a vector of machine names so rgenoud::genoud can setup the cluster automatically.
p_level	print level
s.tol	tolerance level
it.num	the maximum iteration number
pop.size	the initial population size

Examples

GenerateData <- function(n)
{
  x1 <- runif(n, min=-0.5,max=0.5)
  x2 <- runif(n, min=-0.5,max=0.5)
  error <- rnorm(n, sd= 1)
  ph <- exp(-0.5+1*(x1+x2))/(1+exp(-0.5 + 1*(x1+x2)))
  a <- rbinom(n = n, size = 1, prob=ph)
  c <- 1.5 +  + runif(n = n, min=0, max=2)
  cmplt_y <-  pmin(2+x1+x2 +  a*(1 - x1 - x2) +  (0.2 + a*(1+x1+x2)) * error, 4.4)
  censor_y <- pmin(cmplt_y, c)
  delta <- as.numeric(c > cmplt_y)
  return(data.frame(x1=x1,x2=x2,a=a, censor_y = censor_y, delta=delta))
}

                        
          

n <- 400
data <- GenerateData(n)
fit1 <- IPWE_Qopt_DepCen_general(data = data, regimeClass = a~x1+x2, moPropen = a~x1+x2,
                                 tau = 0.2, bw = 20/n, 
                                 pop.size=3000, it.num = 3)
                                 

QTOCen documentation built on June 4, 2019, 5:03 p.m.