predict.SensIAT_within_group_model: Give the Marginal Mean Estimate and its Estimated Asymptotic...

In SensIAT: Sensitivity Analysis for Irregular Assessment Times

Give the Marginal Mean Estimate and its Estimated Asymptotic Variance

Description

Give the marginal mean model estimate

Usage

## S3 method for class 'SensIAT_fulldata_model'
predict(object, time, ...)

## S3 method for class 'SensIAT_within_group_model'
predict(object, time, include.var = TRUE, ..., base = object$base)

Arguments

object	SensIAT_within_group_model object
time	Time points of interest
...	Currently ignored.
include.var	Logical. If TRUE, the variance of the outcome is also returned
base	A SplineBasis object used to evaluate the basis functions.

Value

If include.var is TRUE, a tibble with columns time, mean, and var is returned. otherwise if include.var is FALSE, only the mean vector is returned.

Functions

• predict(SensIAT_fulldata_model): For each combination of time and alpha estimate the mean response and variance for each group as well as estimate the mean treatment effect and variance.

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),
    )
predict(model, time = c(90, 180))

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