Marginal and conditional summary measures: transportability and compatibility across studies

TL;DR

This paper clarifies the differences and implications of marginal and conditional summary measures in study transportability, highlighting potential biases in evidence synthesis due to incompatibility.

Contribution

It provides a detailed analysis of the interpretation, properties, and transportability of various summary measures, emphasizing issues in evidence synthesis.

Findings

Marginal and conditional measures often do not coincide, affecting interpretation.

Covariates not traditionally seen as effect modifiers can influence treatment effects.

Naive pooling of incompatible measures in evidence synthesis can lead to bias.

Abstract

Marginal and conditional summary measures do not generally coincide, have different interpretations and correspond to different decision questions. While these aspects have primarily been recognized for non-collapsible summary measures, they are equally problematic for some collapsible measures in the presence of effect modification. We clarify the interpretation and properties of several marginal and conditional summary measures, considering different types of outcomes and hypothetical outcome-generating mechanisms. We describe implications of the choice of summary measure for transportability, highlighting that covariates not conventionally described as effect modifiers can modify population-level treatment effects. Finally, we illustrate existing summary measure incompatibility issues in the context of evidence synthesis, using the case of covariate adjustment methods for indirect treatment comparisons. Because marginal and conditional summary measures do not generally coincide, their na\"ive pooling in evidence synthesis can produce bias. Almost invariably, care is needed to ensure that evidence synthesis methods are combining compatible summary measures, and this may be easier to accomplish with full access to individual patient data.