get_eff_mod_names

internal

For the problem of indirect treatment comparison with limited subject-level data, this
package provides tools for model-based standardisation with several different computation approaches.
See Remiro‐Azócar A, Heath A, Baio G (2022) "Parametric G‐computation for compatible
indirect treatment comparisons with limited individual patient data",
Res. Synth. Methods, 1–31. ISSN 1759-2879, <doi:10.1002/jrsm.1565>.

Nathan Green

outstandR

Model-Based Standardisation for Indirect Treatment Comparison
with Limited Subject-Level Data

Chengyang Gao

Antonio Remiro-Azocar

get_eff_mod_names function

<dl><dt>formula</dt>
<dd>Linear regression <code>formula</code> object. Prognostic factors (PF) are main effects and effect modifiers (EM) are
interactions with the treatment variable, e.g., y ~ X1 + trt + trt:X2. For covariates as both PF and EM use <code>*</code> syntax.</dd>
<dt>trt_var</dt>
<dd>Treatment variable name; Default 'trt'.</dd></dl>

Arguments

Get effect modifiers — get_eff_mod_names

<dl>

<dt>formula</dt>
<dd>Linear regression <code>formula</code> object. Prognostic factors (PF) are main effects and effect modifiers (EM) are
interactions with the treatment variable, e.g., y ~ X1 + trt + trt:X2. For covariates as both PF and EM use <code>*</code> syntax.</dd>


<dt>trt_var</dt>
<dd>Treatment variable name; Default 'trt'.</dd>

</dl>

get_eff_mod_names: Get effect modifiers

Description

Usage

Value

Arguments