msmdata

This is a simulated dataset of 7500 units with covariates and treatment measured three times and the outcome measured at the end from a hypothetical observational study examining the effect of treatment delivered at each time point on an adverse event.

datasets

Generates balancing weights for causal effect estimation in observational studies with
binary, multi-category, or continuous point or longitudinal treatments by easing and
extending the functionality of several R packages and providing in-house estimation methods.
Available methods include propensity score weighting using generalized linear models, gradient
boosting machines, the covariate balancing propensity score algorithm, Bayesian additive regression trees, and
SuperLearner, and directly estimating balancing weights using entropy balancing, energy balancing, and optimization-based weights. Also
allows for assessment of weights and checking of covariate balance by interfacing directly
with the 'cobalt' package. See the vignette "Installing Supporting Packages" for instructions on how
to install any package 'WeightIt' uses, including those that may not be on CRAN.

Noah Greifer

WeightIt

Weighting for Covariate Balance in Observational Studies

msmdata function

A data frame with 7500 observations on the following 10 variables.<dl>
 <dt><code>X1_0</code></dt>
<dd>a count covariate measured at baseline</dd> <dt><code>X2_0</code></dt>
<dd>a binary covariate measured at baseline</dd> <dt><code>A_1</code></dt>
<dd>a binary indicator of treatment status at the first time point</dd> <dt><code>X1_1</code></dt>
<dd>a count covariate measured at the first time point (after the first treatment)</dd> <dt><code>X2_1</code></dt>
<dd>a binary covariate measured at the first time point (after the first treatment)</dd> <dt><code>A_2</code></dt>
<dd>a binary indicator of treatment status at the second time point</dd> <dt><code>X1_2</code></dt>
<dd>a count covariate measured at the second time point (after the second treatment)</dd> <dt><code>X2_2</code></dt>
<dd>a binary covariate measured at the first time point (after the first treatment)</dd> <dt><code>A_3</code></dt>
<dd>a binary indicator of treatment status at the third time point</dd> <dt><code>Y_B</code></dt>
<dd>a binary indicator of the outcome event (e.g., death)</dd> 
</dl>

Format

Simulated data for a 3 time point sequential study — msmdata

A data frame with 7500 observations on the following 10 variables.<dl>
 <dt><code>X1_0</code></dt>
<dd>a count covariate measured at baseline</dd>

 <dt><code>X2_0</code></dt>
<dd>a binary covariate measured at baseline</dd>

 <dt><code>A_1</code></dt>
<dd>a binary indicator of treatment status at the first time point</dd>

 <dt><code>X1_1</code></dt>
<dd>a count covariate measured at the first time point (after the first treatment)</dd>

 <dt><code>X2_1</code></dt>
<dd>a binary covariate measured at the first time point (after the first treatment)</dd>

 <dt><code>A_2</code></dt>
<dd>a binary indicator of treatment status at the second time point</dd>

 <dt><code>X1_2</code></dt>
<dd>a count covariate measured at the second time point (after the second treatment)</dd>

 <dt><code>X2_2</code></dt>
<dd>a binary covariate measured at the first time point (after the first treatment)</dd>

 <dt><code>A_3</code></dt>
<dd>a binary indicator of treatment status at the third time point</dd>

 <dt><code>Y_B</code></dt>
<dd>a binary indicator of the outcome event (e.g., death)</dd>

 
</dl>

msmdata: Simulated data for a 3 time point sequential study

Description

Usage

Arguments

Format

Examples