Data adaptively discover the shift interventions that require minimal change to achieve target outcome

The find_min_concentrations function provides a g-computation approach to finding minimal shift interventions. This function fits a super learner, then for a grid of exposure levels, predicts the expected outcome. It selects the exposure levels which produce an outcome target within epsilon which are closest to the exposure averages under no shift.

find_min_concentrations(
  data,
  a_names,
  w_names,
  outcome,
  outcome_type,
  mu_learner,
  target_outcome_lvl,
  epsilon,
  seed
)

Arguments

data

A data.frame containing all the variables needed for the analysis, including baseline covariates, exposures, and the outcome.

a_names

A character vector specifying the names of the exposure variables within data.

w_names

A character vector specifying the names of the covariate variables within data.

outcome

The name of the outcome variable in data.

outcome_type

A character string indicating the type of the outcome variable; either "continuous", "binary", or "count".

mu_learner

A list of Lrnr_sl learners specifying the ensemble machine learning models to be used for outcome prediction within the Super Learner framework.

seed

An integer value to set the seed for reproducibility.

deltas

A named list or vector specifying the shift in exposures to define the target parameter. Each element should correspond to an exposure variable specified in a_names, detailing the amount by which that exposure is to be shifted.

top_n

An integer specifying the number of top positive and negative effects to return.

Value

A list containing the top effects and interactions identified and estimated by the function. It includes elements for top positive and negative individual effects as well as top synergistic and antagonistic interactions.

Examples

if (FALSE) {
data <- data.frame(matrix(rnorm(100 * 10), ncol = 10))
names(data) <- c(paste0("X", 1:8), "exposure", "outcome")
deltas <- list(exposure = 0.1)
a_names <- "exposure"
w_names <- paste0("X", 1:8)
outcome <- "outcome"
outcome_type <- "continuous"
mu_learner <- list(sl3::Lrnr_mean$new(), sl3::Lrnr_glm$new())
top_n <- 3
seed <- 123

results <- find_synergy_antagonism(data, deltas, a_names, w_names, outcome, outcome_type, mu_learner, top_n, seed)
print(results)
}