A low-level function for the generic optimization step in estimating dynamic Quanilte-optimal treatment regime for censored data
This function supports wrapper functions for two stage Quantile-optimal treatment regime estimation, namely IPWE_Qopt_DTR_IndCen.
Gene_Quantile_CenIPWE_DTR(data, max, tau, regimeClass.stg1, regimeClass.stg2, s_Diff_Time, txVec1, txVec2, nvars.stg1, nvars.stg2, p.data1, p.data2, sign_beta1.stg1, sign_beta1.stg2, p_level, cluster, s.tol, it.num, pop.size, Domains1 = NULL, Domains2 = NULL, Penalty.level = 0)
data: raw data.frame
max: Maximization (TRUE) or Minimizing (FALSE). Determines if genoud minimizes or maximizes the objective function.
tau: a quantile level of interest
regimeClass.stg1: the class of treatment regimes for stage one
regimeClass.stg2: the class of treatment regimes for stage two
s_Diff_Time: the length of time between the first stage treatment and the second stage treatment
txVec1: the vector of treatment received at the first stage
txVec2: the vector of treatment received at the second stage, it expects entries to be NA for patients who did not receive the second treatment
nvars.stg1: number of coeffients for the decision rule of the first stage
nvars.stg2: number of coeffients for the decision rule of the second stage
p.data1: the design matrix to be used for decision in stage one
p.data2: the design matrix to be used for decision in stage two
sign_beta1.stg1: Is sign of the coefficient for the first non-intercept variable for the first stage known? Default is NULL, meaning user does not have contraint on the sign; FALSE if the coefficient for the first continuous variable is fixed to be -1; TRUE if 1. We can make the search space discrete because we employ scale normalizaion.
sign_beta1.stg2: Default is NULL. Similar to sign_beta1.stg1.
p_level: choose between 0,1,2,3 to indicate different levels of output from the genetic function. Specifically, 0 (minimal printing), 1 (normal), 2 (detailed), and 3 (debug).
cluster: default is FALSE, meaning do not use parallel computing for the genetic algorithm(GA).
s.tol: tolerance level for the GA algorithm. This is input for parameter solution.tolerance
in function rgenoud::genoud.
it.num: the maximum GA iteration number
pop.size: an integer with the default set to be 3000. This is roughly the number individuals for the first generation in the genetic algorithm (rgenoud::genoud).
Domains1: This is optional. If not NULL, please provide the two-column matrix for the searching range of coeffients in stage one. The coefficient taking value of positive/negative one should not be included.
Domains2: This is optional. If not NULL, please provide the two-column matrix for the searching range of coeffients in stage two. The coefficient taking value of positive/negative one should not be included.
Penalty.level: the level that determines which objective function to use. Penalty.level = 0 indicates no regularization; Penalty.level = 1 indicates the value function estimation minus the means absolute average coefficient is the output, which is useful trick to achieve uniqueness of estimated optimal TR when resolution of input response is low.
library(survival) # Simulate data n=200 s_Diff_Time = 1 D <- simJLSDdata(n, case="a") # give regime classes regimeClass.stg1 <- as.formula(a0~x0) regimeClass.stg2 <- as.formula(a1~x1) # extract columns that matches each stage's treatment regime formula p.data1 <- model.matrix(regimeClass.stg1, D) # p.data2 would only contain observations with non-null value. p.data2 <- model.matrix(regimeClass.stg2, D) txVec1 <- D[, "a0"] txVec2 <- D[, "a1"] # Eligibility flag ELG <- (D$censor_y > s_Diff_Time) # Build weights D$deltaC <- 1 - D$delta survfit_all <- survfit(Surv(censor_y, event = deltaC)~1, data=D) survest <- stepfun(survfit_all$time, c(1, survfit_all$surv)) D$ghat <- survest(D$censor_y) g_s_Diff_Time <- survest(s_Diff_Time) D$w_di_vec <- rep(-999, n) for(i in 1:n){ if (!ELG[i]) { D$w_di_vec[i] <- 0.5 * D$ghat[i]} else { D$w_di_vec[i] <- 0.5* D$ghat[i] * 0.5 } } fit1 <- Gene_Quantile_CenIPWE_DTR(data=D, max=TRUE, tau=0.3, regimeClass.stg1 = regimeClass.stg1, regimeClass.stg2 = regimeClass.stg2, s_Diff_Time = s_Diff_Time, txVec1 = txVec1, txVec2 = txVec2, nvars.stg1=2, nvars.stg2=2, p.data1=p.data1, p.data2=p.data2, sign_beta1.stg1=FALSE, sign_beta1.stg2=NULL, p_level=1, cluster=FALSE, s.tol=1e-6, it.num=5, pop.size=6000, Domains1 = NULL, Domains2 = NULL, Penalty.level = 0 )
Useful links