AUTHOR=Polito Letizia , Liang Qixing , Pal Navdeep , Mpofu Philani , Sawas Ahmed , Humblet Olivier , Rufibach Kaspar , Heinzmann Dominik
TITLE=Applying the estimand and target trial frameworks to external control analyses using observational data: a case study in the solid tumor setting
JOURNAL=Frontiers in Pharmacology
VOLUME=15
YEAR=2024
URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2024.1223858
DOI=10.3389/fphar.2024.1223858
ISSN=1663-9812
ABSTRACT=Introduction: In causal inference, the correct formulation of the scientific question of interest is a crucial step. The purpose of this study was to apply causal inference principles to external control analysis using observational data and illustrate the process to define the estimand attributes.
Methods: This study compared long-term survival 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 and similar patients treated with front-line chemotherapy as part of routine clinical care. Causal inference frameworks were applied to define the estimand aligned with the research question and select the estimator to estimate the estimand of interest.
Results: The estimand attributes of the ideal trial were defined using the estimand framework. The target trial framework was used to address specific issues in defining the estimand attributes using observational data from a nationwide electronic health record-derived de-identified database. The two frameworks combined allow to clearly define the estimand and the aligned estimator while accounting for key baseline confounders, index date, and receipt of subsequent therapies. The hazard ratio estimate (point estimate with 95% confidence interval) comparing the randomized clinical trial pooled control arm with the external control was close to 1, which is indicative of similar survival between the two arms.
Discussion: The proposed combined framework provides clarity on the causal contrast of interest and the estimator to adopt, and thus facilitates design and interpretation of the analyses.