Calculation of covariate-conditional treatment effects

Calculates covariate conditional treatment effects using estimated benefit scores

treatment.effects(x, ...)

# S3 method for default
treatment.effects(x, ...)

treat.effects(
  benefit.scores,
  loss = c("sq_loss_lasso", "logistic_loss_lasso", "poisson_loss_lasso",
    "cox_loss_lasso", "owl_logistic_loss_lasso", "owl_logistic_flip_loss_lasso",
    "owl_hinge_loss", "owl_hinge_flip_loss", "sq_loss_lasso_gam",
    "poisson_loss_lasso_gam", "logistic_loss_lasso_gam", "sq_loss_gam",
    "poisson_loss_gam", "logistic_loss_gam", "owl_logistic_loss_gam",
    "owl_logistic_flip_loss_gam", "owl_logistic_loss_lasso_gam",
    "owl_logistic_flip_loss_lasso_gam", "sq_loss_xgboost", "custom"),
  method = c("weighting", "a_learning"),
  pi.x = NULL,
  ...
)

# S3 method for subgroup_fitted
treatment.effects(x, ...)

Arguments

x: a fitted object from fit.subgroup()
...: not used
benefit.scores: vector of estimated benefit scores
loss: loss choice USED TO CALCULATE benefit.scores of both the M function from Chen, et al (2017) and potentially the penalty used for variable selection. See fit.subgroup for more details.
method: method choice USED TO CALCULATE benefit.scores. Either the "weighting" method or "a_learning" method. See fit.subgroup for more details
pi.x: The propensity score for each observation

Value

A List with elements delta (if the treatment effects are a difference/contrast, i.e. \(E[Y|T=1, X] - E[Y|T=-1, X]\)) and gamma (if the treatment effects are a ratio, i.e. \(E[Y|T=1, X] / E[Y|T=-1, X]\))

Examples

library(personalized)

set.seed(123)
n.obs  <- 500
n.vars <- 25
x <- matrix(rnorm(n.obs * n.vars, sd = 3), n.obs, n.vars)


# simulate non-randomized treatment
xbetat   <- 0.5 + 0.5 * x[,21] - 0.5 * x[,11]
trt.prob <- exp(xbetat) / (1 + exp(xbetat))
trt01    <- rbinom(n.obs, 1, prob = trt.prob)

trt      <- 2 * trt01 - 1

# simulate response
delta <- 2 * (0.5 + x[,2] - x[,3] - x[,11] + x[,1] * x[,12])
xbeta <- x[,1] + x[,11] - 2 * x[,12]^2 + x[,13]
xbeta <- xbeta + delta * trt

# continuous outcomes
y <- drop(xbeta) + rnorm(n.obs, sd = 2)

# time-to-event outcomes
surv.time <- exp(-20 - xbeta + rnorm(n.obs, sd = 1))
cens.time <- exp(rnorm(n.obs, sd = 3))
y.time.to.event  <- pmin(surv.time, cens.time)
status           <- 1 * (surv.time <= cens.time)

# create function for fitting propensity score model
prop.func <- function(x, trt)
{
    # fit propensity score model
    propens.model <- cv.glmnet(y = trt,
                               x = x, family = "binomial")
    pi.x <- predict(propens.model, s = "lambda.min",
                    newx = x, type = "response")[,1]
    pi.x
}

subgrp.model <- fit.subgroup(x = x, y = y,
                             trt = trt01,
                             propensity.func = prop.func,
                             loss   = "sq_loss_lasso",
                             nfolds = 3)    # option for cv.glmnet

trt_eff <- treatment.effects(subgrp.model)
str(trt_eff)
#> List of 2
#>  $ delta: num [1:500] 0.533 15.571 1.365 10.705 20.995 ...
#>   ..- attr(*, "comparison.trts")= int 1
#>   ..- attr(*, "reference.trt")= int 0
#>   ..- attr(*, "trts")= int [1:2] 0 1
#>  $ gamma: logi NA
#>  - attr(*, "class")= chr [1:2] "individual_treatment_effects" "list"

trt_eff
#> Summary of individual treatment effects: 
#> E[Y|T=1, X] - E[Y|T=0, X]
#> 
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#> -25.661  -3.245   3.926   4.070  10.995  34.932 


library(survival)
subgrp.model.cox <- fit.subgroup(x = x, y = Surv(y.time.to.event, status),
                           trt = trt01,
                           propensity.func = prop.func,
                           loss   = "cox_loss_lasso",
                           nfolds = 3)              # option for cv.glmnet

trt_eff_c <- treatment.effects(subgrp.model.cox)
str(trt_eff_c)
#> List of 2
#>  $ delta: logi NA
#>  $ gamma: num [1:500] 1.362 0.973 1.527 0.897 1.236 ...
#>   ..- attr(*, "comparison.trts")= int 1
#>   ..- attr(*, "reference.trt")= int 0
#>   ..- attr(*, "trts")= int [1:2] 0 1
#>  - attr(*, "class")= chr [1:2] "individual_treatment_effects" "list"

trt_eff_c
#> Summary of individual treatment effects: 
#> E[Y|T=1, X] / E[Y|T=0, X]
#> 
#> Note: for survival outcomes, the above ratio is 
#> E[g(Y)|T=1, X] / E[g(Y)|T=0, X], 
#> where g() is a monotone increasing function of Y, 
#> the survival time
#> 
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.3656  0.8367  1.0695  1.1264  1.3357  2.6774

Arguments

Value

See also

Examples