Description Usage Arguments Value See Also Examples

View source: R/calculate_treatment_effects.R

Calculates covariate conditional treatment effects using estimated benefit scores

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
treatment.effects(x, ...)
## Default S3 method:
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_gbm", "poisson_loss_gbm",
"logistic_loss_gbm", "cox_loss_gbm", "custom"),
method = c("weighting", "a_learning"), pi.x = NULL, ...)
## S3 method for class 'subgroup_fitted'
treatment.effects(x, ...)
``` |

`x` |
a fitted object from |

`...` |
not used |

`benefit.scores` |
vector of estimated benefit scores |

`loss` |
loss choice USED TO CALCULATE |

`method` |
method choice USED TO CALCULATE |

`pi.x` |
The propensity score for each observation |

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]*)

`fit.subgroup`

for function which fits subgroup identification models.

`print.individual_treatment_effects`

for printing of objects returned by
`treat.effects`

or `treatment.effects`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | ```
library(personalized)
set.seed(123)
n.obs <- 1000
n.vars <- 50
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[,41]
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 = 5) # option for cv.glmnet
trt_eff <- treatment.effects(subgrp.model)
str(trt_eff)
trt_eff
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 = 5) # option for cv.glmnet
trt_eff_c <- treatment.effects(subgrp.model.cox)
str(trt_eff_c)
trt_eff_c
``` |

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