Conflating marginal and conditional treatment effects: Comments on 'Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study'

TL;DR

This paper emphasizes the importance of distinguishing between marginal and conditional treatment effects in population-adjusted indirect comparisons and advocates for developing specific methods for each effect type.

Contribution

It clarifies the distinction between marginal and conditional effects and highlights the need for tailored methodologies depending on the target inference.

Findings

Clarifies the importance of effect type distinction

Highlights methodological considerations for effect estimation

Emphasizes target-specific methodological development

Abstract

In this commentary, we highlight the importance of: (1) carefully considering and clarifying whether a marginal or conditional treatment effect is of interest in a population-adjusted indirect treatment comparison; and (2) developing distinct methodologies for estimating the different measures of effect. The appropriateness of each methodology depends on the preferred target of inference.