O = (W, A, Y)
W = Covariates
A = Treatment (binary or categorical)
Y = Outcome (binary or bounded continuous)
tmle_vimshift_msm(
shift_fxn = shift_additive_bounded,
shift_fxn_inv = shift_additive_bounded_inv,
shift_grid = seq(-1, 1, by = 0.5),
max_shifted_ratio = 2,
weighting = c("identity", "variance"),
...
)
Arguments
| shift_fxn |
A function defining the type of shift to be applied
to the treatment. For an example, see shift_additive. |
| shift_fxn_inv |
A function defining the inverse of the type of
shift to be applied to the treatment. For an example, see
shift_additive_inv. |
| shift_grid |
A numeric vector, specification of a selection of
shifts (on the level of the treatment) to be applied to the intervention.
This is a value passed to the functions above for computing various
values of the outcome under modulated values of the treatment. |
| max_shifted_ratio |
A numeric value indicating the maximum
tolerance for the ratio of the counterfactual and observed intervention
densities. In particular, the shifted value of the intervention is assigned
to a given observational unit when the ratio of counterfactual intervention
density to the observed intervention density is below this value. |
| weighting |
A character indicating the type of weighting used
for construction of the marginal structural model. "identity"
applies the same weight to all individual estimates while "variance"
applies weights based on the inverse variance of the estimate. It would be
expected that variance-based weighting would yield more stable estimates of
the parameter of the MSM. The default remains the identity weighting. |
| ... |
Additional arguments, passed to shift functions. |