sim_point_surv

A dataset with a time-to-event outcome, two baseline nodes, a binary
point treatment, six past-time outcome nodes, and six censoring indicators.

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.

Nick Williams

lmtp

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

Nicholas Williams

Iván Díaz

sim_point_surv function

A data frame with 2000 rows and 16 variables:<dl>
<dt>W1</dt>
<dd>Binary baseline variable.</dd><dt>W2</dt>
<dd>Categorical baseline variable.</dd><dt>trt</dt>
<dd>Binary treatment variable.</dd><dt>C.0</dt>
<dd>Censoring indicator that the observation is observed future time points.</dd><dt>Y.1</dt>
<dd>Outcome node at time 1.</dd><dt>C.1</dt>
<dd>Censoring indicator that the observation is observed future time points.</dd><dt>Y.2</dt>
<dd>Outcome node at time 2.</dd><dt>C.2</dt>
<dd>Censoring indicator that the observation is observed future time points.</dd><dt>Y.3</dt>
<dd>Outcome node at time 3.</dd><dt>C.3</dt>
<dd>Censoring indicator that the observation is observed future time points.</dd><dt>Y.4</dt>
<dd>Outcome node at time 4.</dd><dt>C.4</dt>
<dd>Censoring indicator that the observation is observed future time points.</dd><dt>Y.5</dt>
<dd>Outcome node at time 5.</dd><dt>C.5</dt>
<dd>Censoring indicator that the observation is observed future time points.</dd><dt>Y.6</dt>
<dd>Final outcome node.</dd>
</dl>

Format

Simulated Point-treatment Survival Data — sim_point_surv

A data frame with 2000 rows and 16 variables:<dl>
<dt>W1</dt>
<dd>Binary baseline variable.</dd>

<dt>W2</dt>
<dd>Categorical baseline variable.</dd>

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

<dt>C.0</dt>
<dd>Censoring indicator that the observation is observed future time points.</dd>

<dt>Y.1</dt>
<dd>Outcome node at time 1.</dd>

<dt>C.1</dt>
<dd>Censoring indicator that the observation is observed future time points.</dd>

<dt>Y.2</dt>
<dd>Outcome node at time 2.</dd>

<dt>C.2</dt>
<dd>Censoring indicator that the observation is observed future time points.</dd>

<dt>Y.3</dt>
<dd>Outcome node at time 3.</dd>

<dt>C.3</dt>
<dd>Censoring indicator that the observation is observed future time points.</dd>

<dt>Y.4</dt>
<dd>Outcome node at time 4.</dd>

<dt>C.4</dt>
<dd>Censoring indicator that the observation is observed future time points.</dd>

<dt>Y.5</dt>
<dd>Outcome node at time 5.</dd>

<dt>C.5</dt>
<dd>Censoring indicator that the observation is observed future time points.</dd>

<dt>Y.6</dt>
<dd>Final outcome node.</dd>


</dl>

sim_point_surv: Simulated Point-treatment Survival Data

Description

Usage

Arguments

Format