cal_PEHE: Calculate the PEHE

In riAFTBART: A Flexible Approach for Causal Inference with Multiple Treatments and Clustered Survival Outcomes

Calculate the PEHE

Description

This function calculates the PEHE based on the survival probability from a fitted ri-AFTBART model.

Usage

cal_PEHE(object, metric, time, LP, lambda, eta)

Arguments

object	An object from cal_survprob() function.
metric	A character string representing the metric to be calculated for PEHE. Only "survival" is allowed.
time	A numeric value representing the time point used to calculate PEHE.
LP	A numeric vector corresponding to the true linear predictors for each treatment from the simulated data.
lambda	A numeric value representing the true follow up time for from the simulated data.
eta	A numeric value to induce proportional/non-proportional hazards assumption from the simulated data.

Value

A list with the following three components:

true:	A numeric vector representing the true survival or rmst for each individual.
predicted:	A numeric vector representing the predicted survival or rmst for each individual.
pehe:	A numeric vector representing the calculated pehe.

Examples

library(riAFTBART)
lp_w_all <-
  c(".4*x1 + .1*x2  - .1*x4 + .1*x5",    #' w = 1
    ".2 * x1 + .2 * x2  - .2 * x4 - .3 * x5")  #' w = 2
nlp_w_all <-
  c("-.5*x1*x4  - .1*x2*x5", #' w = 1
    "-.3*x1*x4 + .2*x2*x5")#' w = 2
lp_y_all <- rep(".2*x1 + .3*x2 - .1*x3 - .1*x4 - .2*x5", 3)
nlp_y_all <- rep(".7*x1*x1  - .1*x2*x3", 3)
X_all <- c(
  "rnorm(10, 0, 0.5)",#' x1
  "rbeta(10, 2, .4)",   #' x2
  "runif(10, 0, 0.5)",#' x3
  "rweibull(10,1,2)",  #' x4
  "rbinom(10, 1, .4)"#' x5
)
set.seed(111111)
data <- dat_sim(
  nK = 2,
  K = 5,
  n_trt = 3,
  X = X_all,
  eta = 2,
  lp_y = lp_y_all,
  nlp_y  = nlp_y_all,
  align = FALSE,
  lp_w = lp_w_all,
  nlp_w = nlp_w_all,
  lambda = c(1000,2000,3000),
  delta = c(0.5,0.5),
  psi = 1,
  sigma_w = 1,
  sigma_y = 2,
  censor_rate = 0.1
)
data$LP_true[,1]
data$lambda
data$eta
res <- riAFTBART_fit(M.burnin = 10, M.keep = 10, M.thin = 1, status = data$delta,
                      y.train = data$Tobs, trt.train = data$w, trt.test = 1,
                      x.train = data$covariates,
                      x.test = data$covariates,
                      cluster.id = data$cluster)
res_cal_surv_prob <- cal_surv_prob(object = res,
time.points = 1:max(data$Tobs),
test.only = TRUE,
cluster.id = data$cluster)

res_cal_PEHE_survival <- cal_PEHE(object = res_cal_surv_prob,
                         metric = "survival", time = 40,
                         LP = data$LP_true[,1], lambda = data$lambda[1],
                         eta = data$eta)

res_cal_PEHE_rmst <- cal_PEHE(object = res_cal_surv_prob,
                                  metric = "rmst",
                                  time = 40,
                                  LP = data$LP_true[,1],
                                  lambda = data$lambda[1],
                                  eta = data$eta)
                                  

riAFTBART documentation built on May 17, 2022, 1:07 a.m.