On estimands in target trial emulation

TL;DR

This paper introduces a model-free approach for target trial emulation that emphasizes the importance of estimand choice, improving interpretability and reliability in causal inference from observational data, especially when treatment effects vary.

Contribution

It proposes estimands tailored for different study designs and develops G-computation and IPW estimators, addressing limitations of traditional model-based methods.

Findings

Demonstrates improved clarity and reliability in simulations.

Shows application benefits in ICU antimicrobial de-escalation data.

Highlights advantages over traditional techniques.

Abstract

The target trial framework enables causal inference from longitudinal observational data by emulating randomized trials initiated at multiple time points. Precision is often improved by pooling information across trials, with standard models typically assuming - among other things - a time-constant treatment effect. However, this obscures interpretation when the true treatment effect varies, which we argue to be likely as a result of relying on noncollapsible estimands. To address these challenges, this paper introduces a model-free strategy for target trial analysis, centered around the choice of the estimand, rather than model specification. This ensures that treatment effects remain clearly interpretable for well-defined populations even under model misspecification. We propose estimands suitable for different study designs, and develop accompanying G-computation and inverse probability weighted estimators. Applications on simulations and real data on antimicrobial de-escalation in an intensive care unit setting demonstrate the greater clarity and reliability of the proposed methodology over traditional techniques.