iter: Defining the iter Input Variable

In DynTxRegime: Methods for Estimating Optimal Dynamic Treatment Regimes

Defining the iter Input Variable

Description

Several of the statistical methods implemented in package DynTxRegime allow for an iterative algorithm when completing an outcome regression. This section details how this input is to be defined.

Details

Outcome regression models are specified by the main effects components (moMain) and the contrasts component (moCont). Assuming that the treatment is denoted as binary A, the full regression model is: moMain + A*moCont. There are two ways to fit this model: (i) in the full model formulation (moMain + A*moCont) or (ii) each component, moMain and moCont, is fit separately. iter specifies if (i) or (ii) should be used.

iter >= 1 indicates that moMain and moCont are to be fit separately using an iterative algorithm. iter is the maximum number of iterations. Assume Y = Ymain + Ycont; the iterative algorithm is as follows:

(1) hat(Ycont) = 0;

(2) Ymain = Y - hat(Ycont);

(3) fit Ymain ~ moMain;

(4) set Ycont = Y - hat(Ymain)

(5) fit Ycont ~ A*moCont;

(6) Repeat steps (2) - (5) until convergence or a maximum of iter iterations.

This choice allows the user to specify, for example, a linear main effects component and a non-linear contrasts component.

iter <= 0 indicates that the full model formulation is to be used. The components moMain and moCont will be combined in the package and fit as a single object. Note that if iter <= 0, all non-model components of moMain and moCont must be identical. Specifically, the regression method and any non-default arguments should be identical. By default, the specifications in moMain are used.

DynTxRegime documentation built on Nov. 25, 2023, 1:09 a.m.