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Produce fitted model for group (treatment or control)

Source: R/fulldata_model.R, R/within_group_model.R
fit_SensIAT_within_group_model.Rd

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,
  loss = c("lp_mse", "quasi-likelihood"),
  link = c("identity", "log", "logit"),
  term2_method = c("fast", "original", "fixed_grid", "seeded_adaptive", "gauss_legendre"),
  impute_data = NULL
)

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

loss

The loss function to use. Options are "lp_mse" (default) and "quasi-likelihood". Only used when link is not "identity".

The link function to use. Options are "identity" (default), "log", and "logit". When "identity", the original analytic formula is used; otherwise, the generalized iterative solver is used.

term2_method

Method for computing term2 influence components. Options are: "fast" (default), "original", "fixed_grid", "seeded_adaptive", "gauss_legendre". Only used when link is not "identity".

impute_data

A function that takes (t, df) and returns the imputed data at time t. If NULL (default), a standard imputation function is generated automatically.

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.

A SensIAT_within_group_model object containing:

  • models: List with intensity and outcome sub-models

  • data: The transformed data used for fitting

  • variables: List of variable names (id, outcome, time)

  • End: The end time for the analysis

  • influence: Influence function values by patient

  • alpha: Sensitivity parameter values

  • coefficients: Estimated spline coefficients for each alpha

  • coefficient.variance: Variance of coefficients for each alpha

  • base: The SplineBasis object

  • V_inverse: Inverse of the Gram matrix

  • link: The link function used

  • loss: The loss function used

  • term2_method: The term2 integration method used

Details

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

Functions

  • fit_SensIAT_fulldata_model(): Fit the Marginal Mean Sensitivity Analysis with Tilting Assumption 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{
# Example 1: Default identity link (original analytic solution)
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),
    )

# Example 2: Log link with generalized iterative solver
model_log <-
    fit_SensIAT_within_group_model(
        group.data = SensIAT_example_data,
        outcome_modeler = fit_SensIAT_single_index_fixed_coef_model,
        alpha = 0,
        id = Subject_ID,
        outcome = Outcome,
        time = Time,
        End = 830,
        knots = c(60, 260, 460),
        link = "log",
        loss = "lp_mse"
    )
# }