treated_control_net

This function takes in a list representation of distance matrix
and create a network structure to be solved.

Multivariate matching in observational studies typically has two goals: 1. to construct
treated and control groups that have similar distribution of observed covariates and 2. to produce
matched pairs or sets that are homogeneous in a few priority variables. This packages implements a
network-flow-based method built around a tripartite graph that can simultaneously achieve both goals.
The package also implements a template matching algorithm using a variant of the tripartite graph
design. A brief description of the workflow and some examples are given in the vignette. A more elaborated
tutorial can be found at <https://www.researchgate.net/publication/359513837_Tutorial_for_R_Package_match2C>.

Bo Zhang

match2C

Match One Sample using Two Criteria

treated_control_net function

<dl><dt>n_t</dt>
<dd>Number of treated subjects.</dd>
<dt>n_c</dt>
<dd>Number of controls.</dd>
<dt>dist_list</dt>
<dd>A list representation of the distance matrix.</dd>
<dt>controls</dt>
<dd>Number of controls matched to each treated.</dd></dl>

Arguments

Create a treate-to-control network to be solved via a network flow algorithm. — treated_control_net

<dl>

<dt>n_t</dt>
<dd>Number of treated subjects.</dd>


<dt>n_c</dt>
<dd>Number of controls.</dd>


<dt>dist_list</dt>
<dd>A list representation of the distance matrix.</dd>


<dt>controls</dt>
<dd>Number of controls matched to each treated.</dd>

</dl>

treated_control_net: Create a treate-to-control network to be solved via a network flow algorithm.

Description

Usage

Value

Arguments

Details