# A low-level function for the generic optimization step in estimating Mean-optimal treatment regime for censored data This function supports the `IPWE_mean_IndCen` function. It does the genetic algorithm based method with inverse probability weighting for censored data. In the future, if more complicated applications/scenarios is sought after for mean optimality, users may create their own wrapper function based on `Gene_Mean_CenIPWE`. ```r Gene_Mean_CenIPWE(data_aug, ph, p_level, regimeClass, Domains = NULL, cluster = FALSE, s.tol = 1e-04, it.num = 8, pop.size = 3000) ``` ## Arguments - `data_aug`: a data.frame of the observed data after preprocessing. It should include be augmented with two new columns: `ph` for the enstimated propensity scores and `ghat` for the estimated conditional survival probabilities. - `ph`: propensity score estimates. For example, if the treatment is denoted by `A`, then `ph` should be P(A=1|X) - `p_level`: printing level - `regimeClass`: a formula indicating the form of treatment regimes - `Domains`: default is NULL. Otherwise, the object should be a `nvars *2` matrix used as the space of parameters, which will be supplied to `rgenoud::genoud`. - `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. - `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`). ## Examples ```r 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 <- rep(0.5,n) 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 <- 100 data <- GenerateData(n) # preprocessing data_aug <- data data_aug$ph <- rep(mean(data$a), n) data_aug$deltaC <- 1 - data_aug$delta library(survival) survfit_all <- survfit(Surv(censor_y, event = deltaC)~1, data=data_aug) survest <- stepfun(survfit_all$time, c(1, survfit_all$surv)) data_aug$ghat <- survest(data_aug$censor_y) # estimate the mean-optimal treatment regime meanopt_fit <- Gene_Mean_CenIPWE(data=data_aug, ph = data_aug$ph, p_level=1, regimeClass=a~x1*x2) ```

Gene_Mean_CenIPWE function