basicdata

A dataset consisting of 11,332 observations on 2,500 individuals over 7 time points. Each row in the dataset corresponds to the record of one individual at one time point. Individuals who are censored at time \(k+1\) only have a total of \(k+1\) records, which correspond to time indices \(0,..., k\).

datasets

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 

basicdata function

A data table with 11,332 rows and 8 variables:<dl>
 <dt>t0</dt>
<dd>Time index.</dd> <dt>id</dt>
<dd>Unique identifier for each individual.</dd> <dt>L1</dt>
<dd>Binary time-varying covariate.</dd> <dt>L2</dt>
<dd>Continuous time-varying covariate.</dd> <dt>L3</dt>
<dd>Continuous baseline covariate. For each individual, the baseline values are repeated at each time point.</dd> <dt>A</dt>
<dd>Binary treatment variable.</dd> <dt>D</dt>
<dd>Competing event; time-varying indicator of failure.</dd> <dt>Y</dt>
<dd>Outcome of interest; time-varying indicator of failure.</dd>
</dl>

Format

Example Dataset for a Survival Outcome with Censoring — basicdata

A data table with 11,332 rows and 8 variables:<dl>
 <dt>t0</dt>
<dd>Time index.</dd>

 <dt>id</dt>
<dd>Unique identifier for each individual.</dd>

 <dt>L1</dt>
<dd>Binary time-varying covariate.</dd>

 <dt>L2</dt>
<dd>Continuous time-varying covariate.</dd>

 <dt>L3</dt>
<dd>Continuous baseline covariate. For each individual, the baseline values are repeated at each time point.</dd>

 <dt>A</dt>
<dd>Binary treatment variable.</dd>

 <dt>D</dt>
<dd>Competing event; time-varying indicator of failure.</dd>

 <dt>Y</dt>
<dd>Outcome of interest; time-varying indicator of failure.</dd>


</dl>

Example Dataset for a Survival Outcome with Censoring

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basicdata: Example Dataset for a Survival Outcome with Censoring

Description

Usage

Arguments

Format