msm_vimshift: Working marginal structural model for causal effects of an...
In txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions

Description Usage Arguments Details Value Examples

Working marginal structural model for causal effects of an intervention grid

msm_vimshift(
  W,
  A,
  C_cens = rep(1, length(Y)),
  Y,
  C_samp = rep(1, length(Y)),
  V = NULL,
  delta_grid = seq(-0.5, 0.5, 0.5),
  msm_form = list(type = "linear", knot = NA),
  estimator = c("tmle", "onestep"),
  weighting = c("identity", "variance"),
  ci_level = 0.95,
  ci_type = c("marginal", "simultaneous"),
  ...
)

`W`	A `matrix`, `data.frame`, or similar containing a set of baseline covariates.
`A`	A `numeric` vector corresponding to a treatment variable. The parameter of interest is defined as a location shift of this quantity.
`C_cens`	A `numeric` indicator for whether a given observation was subject to censoring by way of loss to follow-up. The default assumes no censoring due to loss to follow-up.
`Y`	A `numeric` vector of the observed outcomes.
`C_samp`	A `numeric` indicator for whether a given observation was subject to censoring by being omitted from the second-stage sample, used to compute an inverse probability of censoring weighted estimator in such cases. The default assumes no censoring due to two-phase sampling.
`V`	The covariates that are used in determining the sampling procedure that gives rise to censoring. The default is `NULL` and corresponds to scenarios in which there is no censoring (in which case all values in the preceding argument `C` must be uniquely 1. To specify this, pass in a NAMED `list` identifying variables amongst W, A, Y that are thought to have played a role in defining the sampling/censoring mechanism (C).
`delta_grid`	A `numeric` vector giving the individual values of the shift parameter used in computing each of the estimates.
`msm_form`	A `list` specifying the type of working MSM to fit to summarize the counterfactual means. The `list` has two components: (1) `"type"`, which may be either "linear" or "piecewise", and (2) `"knot"`, which, if specified, must be a value in `delta_grid`. See examples for its use.
`estimator`	The type of estimator to be fit, either `"tmle"` for targeted maximum likelihood estimation or `"onestep"` for a one-step augmented inverse probability weighted (AIPW) estimator.
`weighting`	Whether to weight each parameter estimate by the inverse of its variance (in order to improve stability of the resultant MSM fit) or to simply weight all parameter estimates equally. The default is the option `"identity"`, weighting all estimates identically.
`ci_level`	A `numeric` indicating the desired coverage level of the confidence interval to be computed.
`ci_type`	Whether to construct a simultaneous confidence band covering all parameter estimates at once or marginal confidence intervals covering each parameter estimate separately. The default is to construct marginal confidence intervals for each parameter estimate rather than a simultaneous confidence band.
`...`	Additional arguments to be passed to `txshift`.

Computes estimates of the counterfactual mean over a grid of shift stochastic interventions and fits a working marginal structural model to summarize the trend through the counterfactual means as a function of the specified shift intervention. The working marginal structural model may be linear in the shift parameter or piecewise linear with a single knot point. Provides support for two weighting schemes, may be used with either of the one-step or TML estimators, and also allows the construction of marginal or simultaneous confidence intervals.

A list containing estimates of the individual counterfactual means over a grid in the shift parameters (delta_grid), alongside the estimate of a marginal structural model that summarizes a trend through these counterfactual means.

if (require("sl3")) {
  n_obs <- 100
  W <- as.numeric(replicate(1, rbinom(n_obs, 1, 0.5)))
  A <- as.numeric(rnorm(n_obs, mean = 2 * W, sd = 1))
  Y <- rbinom(n_obs, 1, plogis(2 * A - W))
  msm <- msm_vimshift(
    W = W, A = A, Y = Y, estimator = "tmle",
    g_exp_fit_args = list(
      fit_type = "sl",
      sl_learners_density = Lrnr_density_hse$new(Lrnr_glm$new())
    ),
    Q_fit_args = list(
      fit_type = "glm",
      glm_formula = "Y ~ ."
    ),
    delta_grid = seq(-1, 1, 0.25)
  )

  # fit a linear spline with knot at 0
  n_obs <- 100
  W <- as.numeric(replicate(1, rbinom(n_obs, 1, 0.5)))
  A <- as.numeric(rnorm(n_obs, mean = 2 * W, sd = 1))
  Y <- rbinom(n_obs, 1, plogis(0.1 * A * (A >= 0) - 3 * A * (A < 0) - W))
  msm <- msm_vimshift(
    W = W, A = A, Y = Y, estimator = "tmle",
    g_exp_fit_args = list(
      fit_type = "sl",
      sl_learners_density = Lrnr_density_hse$new(Lrnr_glm$new())
    ),
    Q_fit_args = list(
      fit_type = "glm",
      glm_formula = "Y ~ ."
    ),
    delta_grid = seq(-1, 1, 0.25),
    msm_form = list(type = "piecewise", knot = 0)
  )
}