Data-adaptive Discovery of Interactions Based on Joint vs. Individual Shift Interventions

The find_synergy_antagonism function provides a g-computation approach to finding interactions. This implementation fits an SL and then predicts two way joint shifts in exposures and compares this to individual shifts, ranks the interactions based on the difference and then this becomes the interactions we want to estimate using CV-TMLE.

find_synergy_antagonism(
  data,
  deltas,
  a_names,
  w_names,
  outcome,
  outcome_type,
  mu_learner,
  top_n = 3,
  seed
)

Arguments

data

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

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.

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.

top_n

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

seed

An integer value to set the seed for reproducibility.

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)
}