visit_sum

This internal function assists in the implementation of a visit process by creating a covariate,
<code>ts_visit</code>, that counts the number of visits in the past <code>max_visits</code> time points. If this
number is greater than 0, then the individual has not missed more than the maximum number
of visits.

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 

visit_sum function

<dl><dt>pool</dt>
<dd>Data table containing all information prior to time \(t\) (\(t\) noninclusive).</dd>
<dt>histvars</dt>
<dd>Vector of character strings specifying the names of the variables for which lagged cumulative averages are to
be created.</dd>
<dt>time_name</dt>
<dd>Character string specifying the name of the time variable in <code>pool</code>.</dd>
<dt>t</dt>
<dd>Integer specifying the current time index.</dd>
<dt>id_name</dt>
<dd>Character string specifying the name of the ID variable in <code>pool</code>.</dd>
<dt>max_visits</dt>
<dd>A vector of one or more values denoting the maximum number of times
a binary covariate representing a visit process may be missed before
the individual is censored from the data (in the observed data) or
a visit is forced (in the simulated data). Multiple values exist in the
vector when the modeling of more than covariate is attached to a visit
process.</dd></dl>

Arguments

Create Visit Sum Covariate — visit_sum

<dl>

<dt>pool</dt>
<dd>Data table containing all information prior to time \(t\) (\(t\) noninclusive).</dd>


<dt>histvars</dt>
<dd>Vector of character strings specifying the names of the variables for which lagged cumulative averages are to
be created.</dd>


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


<dt>t</dt>
<dd>Integer specifying the current time index.</dd>


<dt>id_name</dt>
<dd>Character string specifying the name of the ID variable in <code>pool</code>.</dd>


<dt>max_visits</dt>
<dd>A vector of one or more values denoting the maximum number of times
a binary covariate representing a visit process may be missed before
the individual is censored from the data (in the observed data) or
a visit is forced (in the simulated data). Multiple values exist in the
vector when the modeling of more than covariate is attached to a visit
process.</dd>

</dl>

Create Visit Sum Covariate

visit_sum: Create Visit Sum Covariate

Description

Usage

Value

Arguments