pred_fun_Y

This internal function fits a generalized linear model (GLM) for the outcome variable using the observed data.

internal

Implements the non-iterative conditional expectation (NICE)
algorithm of the g-formula algorithm (Robins (1986)
<doi:10.1016/0270-0255(86)90088-6>, Hernán and Robins (2024, ISBN:9781420076165)).
The g-formula can estimate an outcome's counterfactual mean or risk under
hypothetical treatment strategies (interventions) when there is sufficient
information on time-varying treatments and confounders.
This package can be used for discrete or continuous time-varying treatments
and for failure time outcomes or continuous/binary end of follow-up
outcomes. The package can handle a random measurement/visit process and a
priori knowledge of the data structure, as well as censoring (e.g., by loss
to follow-up) and two options for handling competing events for failure time
outcomes. Interventions can be flexibly specified, both as interventions on
a single treatment or as joint interventions on multiple treatments.
See McGrath et al. (2020) <doi:10.1016/j.patter.2020.100008> for a guide on
how to use the package.

Sean McGrath

gfoRmula

Parametric G-Formula

Victoria Lin

Zilu Zhang

Roger W. Logan

Lucia C. Petito

Jing Li

McGee Emma

Cheng Carrie

Jessica G. Young

Miguel A. Hernán

2019 The President and Fellows of Harvard College 

pred_fun_Y function

<dl><dt>model</dt>
<dd>Model statement for the outcome variable.</dd>
<dt>yrestrictions</dt>
<dd>List of vectors. Each vector containins as its first entry
a condition and its second entry an integer. When the
condition is <code>TRUE</code>, the outcome variable is simulated
according to the fitted model; when the condition is <code>FALSE</code>,
the outcome variable takes on the value in the second entry.</dd>
<dt>outcome_type</dt>
<dd>Character string specifying the "type" of the outcome. The possible "types" are: <code>"survival"</code>, <code>"continuous_eof"</code>, and <code>"binary_eof"</code>.</dd>
<dt>outcome_name</dt>
<dd>Character string specifying the name of the outcome variable in <code>obs_data</code>.</dd>
<dt>time_name</dt>
<dd>Character string specifying the name of the time variable in <code>obs_data</code>.</dd>
<dt>obs_data</dt>
<dd>Data on which the model is fit.</dd>
<dt>model_fits</dt>
<dd>Logical scalar indicating whether to return the fitted models. The default is <code>FALSE</code>.</dd>
<dt>ymodel_fit_custom</dt>
<dd>Function specifying a custom outcome model. See the vignette "Using Custom Outcome Models in gfoRmula" for details.</dd></dl>

Arguments

Fit Outcome Model — pred_fun_Y

<dl>

<dt>model</dt>
<dd>Model statement for the outcome variable.</dd>


<dt>yrestrictions</dt>
<dd>List of vectors. Each vector containins as its first entry
a condition and its second entry an integer. When the
condition is <code>TRUE</code>, the outcome variable is simulated
according to the fitted model; when the condition is <code>FALSE</code>,
the outcome variable takes on the value in the second entry.</dd>


<dt>outcome_type</dt>
<dd>Character string specifying the "type" of the outcome. The possible "types" are: <code>"survival"</code>, <code>"continuous_eof"</code>, and <code>"binary_eof"</code>.</dd>


<dt>outcome_name</dt>
<dd>Character string specifying the name of the outcome variable in <code>obs_data</code>.</dd>


<dt>time_name</dt>
<dd>Character string specifying the name of the time variable in <code>obs_data</code>.</dd>


<dt>obs_data</dt>
<dd>Data on which the model is fit.</dd>


<dt>model_fits</dt>
<dd>Logical scalar indicating whether to return the fitted models. The default is <code>FALSE</code>.</dd>


<dt>ymodel_fit_custom</dt>
<dd>Function specifying a custom outcome model. See the vignette "Using Custom Outcome Models in gfoRmula" for details.</dd>

</dl>

Fit Outcome Model

pred_fun_Y: Fit Outcome Model

Description

Usage

Value

Arguments