Produce fitted model for group (treatment or control) — fit_SensIAT_fulldata

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
)

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: A separate function that may be swapped out to switch between negative-binomial, single index model, or another we will dream up in the future.
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.

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.

Details

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

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

# \donttest{
model <-
    fit_SensIAT_within_group_model(
        group.data = SensIAT_example_data,
        outcome_modeler = SensIAT_sim_outcome_modeler,
        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),
    )
# }