Title

Title

Usage

SensIAT_fit_marginal_model(
  data,
  id,
  alpha,
  knots,
  outcome.model,
  intensity.model,
  spline.degree = 3L,
  ...
)

Arguments

data

Data for evaluation of the model. Should match the data used to fit the intensity and outcome models.

id

The subject identifier variable in the data. Lazy evaluation is used, so it can be a symbol or a string.

alpha

Sensitivity parameter, a vector of values.

knots

Location of spline knots. If a SplineBasis object is provided, it is used directly.

outcome.model

The observed effects model.

intensity.model

The assessment time intensity model.

spline.degree

The degree of the spline basis, default is 3 (cubic splines).

...

Additional arguments passed to compute_influence_terms.

Value

a list with the fitted model, including the coefficients and their variances for each alpha value.

Examples

# Note: example takes approximately 30 seconds to run.
# \donttest{
library(survival)
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
library(splines)
# Create followup data with lags
# added variables `..prev_time..`, `..delta_time..` and `..prev_outcome..`
# have special interpretations when computing the influence.
data_with_lags <- SensIAT_example_data |>
    dplyr::group_by(Subject_ID) |>
    dplyr::mutate(
        ..prev_outcome.. = dplyr::lag(Outcome, default = NA_real_, order_by = Time),
        ..prev_time.. = dplyr::lag(Time, default = 0, order_by = Time),
        ..delta_time.. = Time - dplyr::lag(.data$Time, default = NA_real_, order_by = Time)
    )

# Create the observation time intensity model
intensity.model <-
    coxph(Surv(..prev_time.., Time, !is.na(Outcome)) ~ ..prev_outcome.. + strata(Visit),
    data = data_with_lags |> dplyr::filter(.data$Time > 0))

# Create the observed outcome model
outcome.model <-
    SensIAT_sim_outcome_modeler(
        Outcome ~ ns(..prev_outcome.., df=3) + ..delta_time.. - 1,
        id = Subject_ID,
        data = data_with_lags |> filter(Time > 0))

# Fit the marginal outcome model
mm <- SensIAT_fit_marginal_model(
    data = data_with_lags,
    id = Subject_ID,
    alpha = c(-0.6, -0.3, 0, 0.3, 0.6),
    knots = c(60, 260, 460),
    intensity.model = intensity.model,
    time.vars = c('..delta_time..'),
    outcome.model = outcome.model)
# }