BootEstimation_MT

This function obtains the estimates of mediation effects via non-parametric bootstrapping.
Through bootstrap sampling and repeating the algorithm of function <code>SingleEstimation</code>,
This function obtains a number of estimates for each type of effect.
This is an internal function, automatically called by the function <code>Statistics</code>.

This program realizes a universal estimation approach that accommodates
multi-category variables and effect scales, making up for the deficiencies
of the existing approaches when dealing with non-binary exposures and
complex models. The estimation via bootstrapping can simultaneously provide results of
causal mediation on risk difference (RD), odds ratio (OR) and risk ratio (RR) scales with tests of the effects' difference.
The estimation is also applicable to many other settings, e.g., moderated mediation, inconsistent covariates,
panel data, etc. The high flexibility and compatibility
make it possible to apply for any type of model, greatly meeting the needs of
current empirical researches.

Tianbao Zhou

unvs.med

A Universal Approach for Causal Mediation Analysis

Xinghao Li

Lin Liu*

BootEstimation_MT function

<dl><dt>m_model</dt>
<dd>a fitted model object for the mediator.</dd>
<dt>y_model</dt>
<dd>a fitted model object for the outcome.</dd>
<dt>data</dt>
<dd>a dataframe used in the analysis.</dd>
<dt>X</dt>
<dd>a character variable of the exposure's name.</dd>
<dt>exp0</dt>
<dd>a numeric variable of the baseline level of the exposure.</dd>
<dt>exp1</dt>
<dd>a numeric variable of the new level of the exposure.</dd>
<dt>M</dt>
<dd>a character variable of the mediator's name.</dd>
<dt>Y</dt>
<dd>a character variable of the outcome's name.</dd>
<dt>m_type</dt>
<dd>a character variable of the mediator's type.</dd>
<dt>y_type</dt>
<dd>a character variable of the outcome's type.</dd>
<dt>boot_num</dt>
<dd>the times of bootstrapping in the analysis. The default is 100.</dd></dl>

Arguments

Bootstrapping Estimation for Causal Mediation Effects via
Multi-threading Process — BootEstimation_MT

<dl>

<dt>m_model</dt>
<dd>a fitted model object for the mediator.</dd>


<dt>y_model</dt>
<dd>a fitted model object for the outcome.</dd>


<dt>data</dt>
<dd>a dataframe used in the analysis.</dd>


<dt>X</dt>
<dd>a character variable of the exposure's name.</dd>


<dt>exp0</dt>
<dd>a numeric variable of the baseline level of the exposure.</dd>


<dt>exp1</dt>
<dd>a numeric variable of the new level of the exposure.</dd>


<dt>M</dt>
<dd>a character variable of the mediator's name.</dd>


<dt>Y</dt>
<dd>a character variable of the outcome's name.</dd>


<dt>m_type</dt>
<dd>a character variable of the mediator's type.</dd>


<dt>y_type</dt>
<dd>a character variable of the outcome's type.</dd>


<dt>boot_num</dt>
<dd>the times of bootstrapping in the analysis. The default is 100.</dd>

</dl>

BootEstimation_MT: Bootstrapping Estimation for Causal Mediation Effects via Multi-threading Process

Description

Usage

Value

Arguments

Details