Function to predict either benefit scores or treatment recommendations

Predicts benefit scores or treatment recommendations based on a fitted subgroup identification model

Function to obtain predictions for weighted ksvm objects

# S3 method for subgroup_fitted
predict(
  object,
  newx,
  type = c("benefit.score", "trt.group"),
  cutpoint = 0,
  ...
)

# S3 method for wksvm
predict(object, newx, type = c("class", "linear.predictor"), ...)

Arguments

object

fitted object returned by validate.subgrp() function.

For predict.wksvm(), this should be a fitted wksvm object from the weighted.ksvm() function

newx

new design matrix for which predictions will be made

type

type of prediction. type = "benefit.score" results in predicted benefit scores and type = "trt.group" results in prediction of recommended treatment group.

For predict.wksvm(), type = 'class' yields predicted class and type = 'linear.predictor' yields estimated function (the sign of which is the estimated class)

cutpoint

numeric value for patients with benefit scores above which (or below which if larger.outcome.better = FALSE) will be recommended to be in the treatment group. Can also set cutpoint = "median", which will use the median value of the benefit scores as the cutpoint or can set specific quantile values via "quantx" where "x" is a number between 0 and 100 representing the quantile value; e.g. cutpoint = "quant75" will use the 75th perent upper quantile of the benefit scores as the quantile.

...

not used

Examples

library(personalized)

set.seed(123)
n.obs  <- 500
n.vars <- 15
x <- matrix(rnorm(n.obs * n.vars, sd = 3), n.obs, n.vars)


# simulate non-randomized treatment
xbetat   <- 0.5 + 0.5 * x[,11] - 0.5 * x[,3]
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)

# 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

subgrp.model$subgroup.trt.effects
#> $subgroup.effects
#> Est of E[Y|T=0,Recom=0]-E[Y|T=/=0,Recom=0] 
#>                                   13.62195 
#> Est of E[Y|T=1,Recom=1]-E[Y|T=/=1,Recom=1] 
#>                                   20.46271 
#> 
#> $avg.outcomes
#>            Recommended 0 Recommended 1
#> Received 0     -15.61725    -29.986895
#> Received 1     -29.23920     -9.524184
#> 
#> $sample.sizes
#>            Recommended 0 Recommended 1
#> Received 0           103           115
#> Received 1           121           161
#> 
#> $overall.subgroup.effect
#> [1] 17.43788
#> 
benefit.scores <- predict(subgrp.model, newx = x, type = "benefit.score")

rec.trt.grp <- predict(subgrp.model, newx = x, type = "trt.group")

Function to predict either benefit scores or treatment recommendations

Arguments

See also

Examples