dummize_ipd

This is a convenient function to convert categorical variables into dummy binary variables.
This would be especially useful if the variable has more than two factors.
Note that the original variable is kept after a variable is dummized.

Facilitates performing matching adjusted indirect comparison
(MAIC) analysis where the endpoint of interest is either time-to-event
(e.g. overall survival) or binary (e.g. objective tumor response). The method
is described by Signorovitch et al (2012) <doi:10.1016/j.jval.2012.05.004>.

Isaac Gravestock

maicplus

Matching Adjusted Indirect Comparison

Gregory Chen

Michael Seo

Miranta Antoniou

Chrysostomos Kalyvas

MSD, Inc. 

F. Hoffmann-La Roche AG 

dummize_ipd function

<dl><dt>raw_ipd</dt>
<dd>ipd data that contains variable to dummize</dd>
<dt>dummize_cols</dt>
<dd>vector of column names to binarize</dd>
<dt>dummize_ref_level</dt>
<dd>vector of reference level of the variables to binarize</dd></dl>

Arguments

Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd

<dl>

<dt>raw_ipd</dt>
<dd>ipd data that contains variable to dummize</dd>


<dt>dummize_cols</dt>
<dd>vector of column names to binarize</dd>


<dt>dummize_ref_level</dt>
<dd>vector of reference level of the variables to binarize</dd>

</dl>

dummize_ipd: Create dummy variables from categorical variables in an individual patient data (ipd)

Description

Usage

Value

Arguments

Examples