Computes treatment effects within various subgroups

Computes treatment effects within various subgroups to estimate subgroup treatment effects

subgroup.effects(
  benefit.scores,
  y,
  trt,
  pi.x,
  cutpoint = 0,
  larger.outcome.better = TRUE,
  reference.trt = NULL
)

Arguments

benefit.scores

vector of estimated benefit scores

y

The response vector

trt

treatment vector with each element equal to a 0 or a 1, with 1 indicating treatment status is active.

pi.x

The propensity score for each observation

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.

larger.outcome.better

boolean value of whether a larger outcome is better. Set to TRUE if a larger outcome is better and set to FALSE if a smaller outcome is better. Defaults to TRUE.

reference.trt

index of which treatment is the reference (in the case of multiple treatments). This should be known already, as for a trt with K-levels, there will be K-1 benefit scores (1 per column) of benefit.scores, where each column is a comparison of each K-1 treatments with the reference treatment. The default is the last level of trt if it is a factor.

See also

fit.subgroup for function which fits subgroup identification models which generate benefit scores.