rmse_calculate

This internal function calculates root mean square error (RMSE) for the covariate (and the outcome and competing event, if applicable) models fit on
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 

rmse_calculate function

<dl><dt>i</dt>
<dd>Integer specifying the index of <code>fits</code>.</dd>
<dt>fits</dt>
<dd>List of fitted models.</dd>
<dt>covnames</dt>
<dd>Vector of character strings specifying the names of the time-varying covariates in <code>obs_data</code>.</dd>
<dt>covtypes</dt>
<dd>Vector of character strings specifying the "type" of each time-varying covariate included in <code>covnames</code>. The possible "types" are: <code>"binary"</code>, <code>"normal"</code>, <code>"categorical"</code>, <code>"bounded normal"</code>, <code>"zero-inflated normal"</code>, <code>"truncated normal"</code>, <code>"absorbing"</code>, <code>"categorical time"</code>, and <code>"custom"</code>.</dd></dl>

Arguments

Calculate RMSE for Covariate, Outcome, and Competing Risk Models — rmse_calculate

<dl>

<dt>i</dt>
<dd>Integer specifying the index of <code>fits</code>.</dd>


<dt>fits</dt>
<dd>List of fitted models.</dd>


<dt>covnames</dt>
<dd>Vector of character strings specifying the names of the time-varying covariates in <code>obs_data</code>.</dd>


<dt>covtypes</dt>
<dd>Vector of character strings specifying the "type" of each time-varying covariate included in <code>covnames</code>. The possible "types" are: <code>"binary"</code>, <code>"normal"</code>, <code>"categorical"</code>, <code>"bounded normal"</code>, <code>"zero-inflated normal"</code>, <code>"truncated normal"</code>, <code>"absorbing"</code>, <code>"categorical time"</code>, and <code>"custom"</code>.</dd>

</dl>

Calculate RMSE for Covariate, Outcome, and Competing Risk Models

rmse_calculate: Calculate RMSE for Covariate, Outcome, and Competing Risk Models

Description

Usage

Value

Arguments