Summarizes covariate values within the estimated subgroups

summarize.subgroups(x, ...)

# S3 method for default
summarize.subgroups(x, subgroup, ...)

# S3 method for subgroup_fitted
summarize.subgroups(x, ...)

Arguments

x

a fitted object from fit.subgroup() or a matrix of covariate values

...

optional arguments to summarize.subgroups methods

subgroup

vector of indicators of same length as the number of rows in x if x is a matrix. A value of 1 in the ith position of subgroup indicates patient i is in the subgroup of patients recommended the treatment and a value of 0 in the ith position of subgroup indicates patient i is in the subgroup of patients recommended the control. If x is a fitted object returned by fit.subgroup(), subgroup is not needed.

Details

The p-values shown are raw p-values and are not adjusted for multiple comparisons.

See also

fit.subgroup for function which fits subgroup identification models and print.subgroup_summary for arguments for printing options for summarize.subgroups().

Examples

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 comp <- summarize.subgroups(subgrp.model) print(comp, p.value = 0.01)
#> Avg (recom 0) Avg (recom 1) 0 - 1 SE (recom 0) SE (recom 1) #> V2 -1.3890 1.9147 -3.3037 0.1124 0.1146 #> V3 1.4399 -1.8286 3.2686 0.1067 0.1118 #> V13 -0.5653 0.2542 -0.8195 0.1282 0.1405
# or we can simply supply the matrix x and the subgroups comp2 <- summarize.subgroups(x, subgroup = 1 * (subgrp.model$benefit.scores > 0)) print(comp2, p.value = 0.01)
#> Avg (recom 0) Avg (recom 1) 0 - 1 SE (recom 0) SE (recom 1) #> V2 -1.3890 1.9147 -3.3037 0.1124 0.1146 #> V3 1.4399 -1.8286 3.2686 0.1067 0.1118 #> V13 -0.5653 0.2542 -0.8195 0.1282 0.1405