predict_binomial

This internal function simulates covariate values from a binomial distribution.

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 

predict_binomial function

<dl><dt>x</dt>
<dd>Integer specifying the number of observations to be simulated.</dd>
<dt>size</dt>
<dd>Integer specifying the number of trials.</dd>
<dt>prob</dt>
<dd>Numeric vector specifying the probabilities.</dd></dl>

Arguments

Simulate Binary Values — predict_binomial

<dl>

<dt>x</dt>
<dd>Integer specifying the number of observations to be simulated.</dd>


<dt>size</dt>
<dd>Integer specifying the number of trials.</dd>


<dt>prob</dt>
<dd>Numeric vector specifying the probabilities.</dd>

</dl>

Simulate Binary Values

predict_binomial: Simulate Binary Values

Description

Usage

Value

Arguments