Description Usage Arguments See Also Examples

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

Function to obtain predictions for weighted ksvm objects

1 2 3 4 5 6 7 |

`object` |
fitted object returned by For |

`newx` |
new design matrix for which predictions will be made |

`type` |
type of prediction. For |

`cutpoint` |
numeric value for patients with benefit scores above which
(or below which if |

`...` |
not used |

`fit.subgroup`

for function which fits subgroup identification models.

`weighted.ksvm`

for fitting `weighted.ksvm`

objects

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 | ```
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)
# 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
subgrp.model$subgroup.trt.effects
benefit.scores <- predict(subgrp.model, newx = x, type = "benefit.score")
rec.trt.grp <- predict(subgrp.model, newx = x, type = "trt.group")
``` |

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