Applying the Estimand and Target Trial frameworks to external control analyses using observational data: a case study in the solid tumor setting

TL;DR

This paper demonstrates how combining the estimand and target trial frameworks enhances causal inference in observational studies by clarifying the scientific question and addressing confounding, using a case study in lung cancer treatment.

Contribution

It introduces a combined approach of estimand and target trial frameworks to improve the design and interpretation of observational analyses in clinical research.

Findings

Framework clarifies causal contrast and estimator choice.

Application to lung cancer data shows improved analysis clarity.

Addresses confounding and treatment switching issues.

Abstract

In causal inference, the correct formulation of the scientific question of interest is a crucial step. Here we apply the estimand framework to a comparison of the outcomes of patient-level clinical trials and observational data to help structure the clinical question. In addition, we complement the estimand framework with the target trial framework to address specific issues in defining the estimand attributes using observational data and discuss synergies and differences of the two frameworks. Whereas the estimand framework proves useful to address the challenge that in clinical trials and routine clinical practice patients may switch to subsequent systemic therapies after the initially assigned systematic treatment, the target trial framework supports addressing challenges around baseline confounding and the index date. We apply the combined framework to compare long-term outcomes of a pooled set of three previously reported randomized phase 3 trials studying patients with metastatic non-small cell lung cancer receiving front-line chemotherapy (randomized clinical trial cohort) and similar patients treated with front-line chemotherapy as part of routine clinical care (observational comparative cohort). We illustrate the process to define the estimand attributes and select the estimator to estimate the estimand of interest while accounting for key baseline confounders, index date, and receipt of subsequent therapies. The proposed combined framework provides more clarity on the causal contrast of interest and the estimator to adopt and thus facilitates design and interpretation of the analyses.