guess_treatment_name

Does a variable appear more than once in interactions?
If not then pick first LHS interaction term.
Finally, if there are no interactions then pick last main effect term.

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

guess_treatment_name 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></dl>

Arguments

Guess treatment name — guess_treatment_name

<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>

</dl>

guess_treatment_name: Guess treatment name

Description

Usage

Value

Arguments