InterXshift

Under a fixed shift to exposures identify using g-computation the joint shift of pairwise exposures in a mixed exposure compared to the additive individual shifts. Positive values indicate synergy and negative antagonism, get the top synergy and antagonism results and use CV-TMLE to efficiently estimate the interaction target parameter.

InterXshift(
  w,
  a,
  y,
  deltas,
  estimator = "tmle",
  fluctuation = "standard",
  var_sets = NULL,
  pi_learner = NULL,
  mu_learner = NULL,
  g_learner = NULL,
  e_learner = NULL,
  zeta_learner = NULL,
  n_folds = 2,
  outcome_type = "continuous",
  parallel = TRUE,
  parallel_type = "multi_session",
  num_cores = 2,
  seed = seed,
  hn_trunc_thresh = 10,
  adaptive_delta = FALSE,
  discover_only = FALSE,
  top_n = 2
)

Arguments

w

A matrix, data.frame, or similar containing a set of baseline covariates. These variables are measured before exposures.

a

matrix, data.frame, or similar containing individual or multiple exposures.

y

numeric vector of observed outcomes.

deltas

A numeric value indicating the shift in exposures to define the target parameter, with respect to the scale of the exposures (A). If adaptive_delta is true, these values will be reduced.

estimator

The type of estimator to fit: "tmle" for targeted maximum likelihood estimation, or "onestep" for a one-step estimator.

fluctuation

Method used in the targeting step for TML estimation: "standard" or "weighted". This determines where to place the auxiliary covariate in the logistic tilting regression.

var_sets

A list specifying variable sets for deterministic InterXshift usage. Example: var_sets <- c("A_1", "A_1-Z_2") where the analyst provides variable sets for exposures, exposure-mediator, or exposure-covariate relationships.

pi_learner

Learners for fitting Super Learner ensembles to densities via sl3.

mu_learner

Learners for fitting Super Learner ensembles to the outcome model via sl3.

g_learner

Learners for fitting Super Learner ensembles to the g-mechanism g(A|W) (a probability estimator, not a density estimator) for mediation via sl3.

e_learner

Learners for fitting Super Learner ensembles to the e-mechanism g(A|Z,W) (a probability estimator, not a density estimator) for mediation via sl3.

zeta_learner

Learners for fitting Super Learner ensembles to the outcome model via sl3..

n_folds

Number of folds to use in cross-validation, default is 2.

outcome_type

Data type of the outcome, default is "continuous".

parallel

Whether to parallelize across cores (default: TRUE).

parallel_type

Type of parallelization to use if parallel is TRUE: "multi_session" (default), "multicore", or "sequential".

num_cores

Number of CPU cores to use in parallelization (default: 2).

seed

numeric seed value to be passed to all functions.

hn_trunc_thresh

Truncation level for the clever covariate (default: 10).

adaptive_delta

If TRUE, reduces the user-specified delta until the Hn calculated for a shift does not have any observation greater than hn_trunc_thresh (default: FALSE).

z

matrix, data.frame, or similar containing individual or multiple mediators (optional).

Value

An S3 object of class InterXshift containing the results of the procedure to compute a TML or one-step estimate of the counterfactual mean under a modified treatment policy that shifts a continuous-valued exposure by a scalar amount delta. These exposures are data-adaptively identified using the CV-TMLE procedure.