Fit outcome regression (Q-function) models across all stages for a single regime

Fits Q-function models backwards from the last stage to the first for a single treatment regime. At each stage, both regime-modified and unmodified predicted values are computed. Handles feasible sets (where responders may not be re-randomized) and interim analyses.

Usage

get_q_fits(df, q_list, regime, feasible_sets_indicator = FALSE)

Arguments

df

A data frame containing the trial data, including treatment assignments, covariates, outcomes (y), and a kappa column.

q_list

A list of outcome regression model specifications (one per stage). Each element is either a modelObj object or a named list with elements "r0" and "r1" for non-responders and responders, respectively.

regime

A matrix of treatment assignments under the regime being evaluated, with columns corresponding to stages.

feasible_sets_indicator

A logical value indicating whether feasible sets are present. Default is FALSE. If individuals are not re-randomized (i.e. for some stage a response status prevents individuals from receiving a random treatment), then feasible_sets_indicator should be set to TRUE.

Value

A list with the following components:

q_fits

A list of fitted modelObj objects, one per stage. When feasible sets with multiple models are used, the element is a named list with "r0" and "r1".

mod_regime_vhats

A matrix of regime-modified predicted values with columns q1, ..., q_{K+1}.

unmod_regime_vhats

A matrix of unmodified predicted values with columns q1_nochange, ..., q_{K+1}_nochange.