multivariate_data

A dataset with a continuous outcome, three baseline covariates,
and two treatment variables.

datasets

Non-parametric estimators for casual effects based on longitudinal modified treatment
policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment,
and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are
censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure
irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects
can be calculated and relative risks and odds ratios may be calculated for binary outcomes.
Supports survival outcomes with competing risks (Diaz, Hoffman, and Hejazi; <doi:10.1007/s10985-023-09606-7>).

Nick Williams

lmtp

Non-Parametric Causal Effects of Feasible Interventions Based on
Modified Treatment Policies

Nicholas Williams

Iván Díaz

multivariate_data function

A data frame with 2000 rows and 6 variables:<dl>
<dt>C1</dt>
<dd>Continuous baseline variable.</dd><dt>C2</dt>
<dd>Continuous baseline variable.</dd><dt>C3</dt>
<dd>Continuous baseline variable.</dd><dt>D1</dt>
<dd>Treatment variable one at baseline.</dd><dt>D2</dt>
<dd>Treatment variable two at baseline.</dd><dt>Y</dt>
<dd>Continuous outcome</dd>
</dl>

Format

Simulated Multivariate Exposure Data — multivariate_data

A data frame with 2000 rows and 6 variables:<dl>
<dt>C1</dt>
<dd>Continuous baseline variable.</dd>

<dt>C2</dt>
<dd>Continuous baseline variable.</dd>

<dt>C3</dt>
<dd>Continuous baseline variable.</dd>

<dt>D1</dt>
<dd>Treatment variable one at baseline.</dd>

<dt>D2</dt>
<dd>Treatment variable two at baseline.</dd>

<dt>Y</dt>
<dd>Continuous outcome</dd>


</dl>

multivariate_data: Simulated Multivariate Exposure Data

Description

Usage

Arguments

Format