fit_SensIAT_within_group_model: Produce fitted model for group (treatment or control)

In SensIAT: Sensitivity Analysis for Irregular Assessment Times

Produce fitted model for group (treatment or control)

Description

Produces a fitted model that may be used to produce estimates of mean and variance for the given group.

Usage

fit_SensIAT_fulldata_model(data, trt, ...)

fit_SensIAT_within_group_model(
  group.data,
  outcome_modeler,
  id,
  outcome,
  time,
  knots,
  alpha = 0,
  End = NULL,
  intensity.args = list(),
  outcome.args = list(),
  influence.args = list(),
  spline.degree = 3,
  add.terminal.observations = TRUE
)

Arguments

data	the full data set.
trt	an expression that determine what is treated as the treatment. Everything not treatment is considered control.
...	common arguments passed to fit_SensIAT_within_group_model.
group.data	The data for the group that is being analyzed. Preferably passed in as a single tibble that internally is subsetted/filtered as needed.
outcome_modeler	function for fitting the outcome model. Called with a formula, data argument and outcome.args list.
id	The variable that identifies the patient.
outcome	The variable that contains the outcome.
time	The variable that contains the time.
knots	knot locations for defining the spline basis.
alpha	The sensitivity parameter.
End	The end time for this data analysis, we need to set the default value as the max value of the time.
intensity.args	A list of optional arguments for intensity model. See the Intensity Arguments section.
outcome.args	parameters as needed passed into the outcome_modeler. One special element may be 'model.modifications' which, if present, should be a formula that will be used to modify the outcome model per, update.formula.
influence.args	A list of optional arguments used when computing the influence. See the Influence Arguments section.
spline.degree	The degree of the spline basis.
add.terminal.observations	Logical indicating whether to add terminal observations to the data. If TRUE, data may not contain any NAs. if FALSE, data will be assumed to already include the terminal observations

Details

This function should be agnostic to whether it is being provided a treatment or control group.

Value

a list with class SensIAT-fulldata-fitted-model with two components, control and treatment, each of which is an independently fitted SensIAT-within-group-fitted-model fit with the fit_within_group_model function.

Should return everything needed to define the fit of the model. This can then be used for producing the estimates of mean, variance, and in turn treatment effect. For the full data model a list with two models one each for the treatment and control groups.

Functions

• fit_SensIAT_fulldata_model(): Fit the sensitivity analysis for both treatment and control groups.

Intensity Arguments

The intensity.args list may contain the following elements:

• model.modifications A formula that will be used to modify the intensity model from it's default, per update.formula.

• kernel The kernel function for the intensity model. Default is the Epanechnikov kernel.

• bandwidth The bandwidth for the intensity model kernel.

Influence Arguments

The influence.args list may contain the following elements:

• method The method for integrating, adaptive or fixed quadrature. Default is 'adaptive'.

• tolerance The tolerance when using adaptive quadrature.

• delta The bin width for fixed quadrature.

• resolution alternative to delta by specifying the number of bins.

• fix_discontinuity Whether to account for the discontinuity in the influence at observation times.

Examples

model <-
    fit_SensIAT_within_group_model(
        group.data = SensIAT_example_data,
        outcome_modeler = fit_SensIAT_single_index_fixed_coef_model,
        alpha = c(-0.6, -0.3, 0, 0.3, 0.6),
        id = Subject_ID,
        outcome = Outcome,
        time = Time,
        End = 830,
        knots = c(60,260,460),
    )

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