fit_SensIAT_marginal_mean_model: Fit the Marginal Means Model

In SensIAT: Sensitivity Analysis for Irregular Assessment Times

Fit the Marginal Means Model

Description

Fit the Marginal Means Model

Usage

fit_SensIAT_marginal_mean_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.

library(survival)
library(dplyr)
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 <-
    fit_SensIAT_single_index_fixed_coef_model(
        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 <- fit_SensIAT_marginal_mean_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)

SensIAT documentation built on Sept. 9, 2025, 5:50 p.m.