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In causal inference, the correct formulation of the scientific question ofinterest is a crucial step. Here we apply the estimand framework to acomparison of the outcomes of patient-level clinical trials and observationaldata to help structure the clinical question. In addition, we complement theestimand framework with the target trial framework to address specific issuesin defining the estimand attributes using observational data and discusssynergies and differences of the two frameworks. Whereas the estimand frameworkproves useful to address the challenge that in clinical trials and routineclinical practice patients may switch to subsequent systemic therapies afterthe initially assigned systematic treatment, the target trial frameworksupports addressing challenges around baseline confounding and the index date.We apply the combined framework to compare long-term outcomes of a pooled setof three previously reported randomized phase 3 trials studying patients withmetastatic non-small cell lung cancer receiving front-line chemotherapy(randomized clinical trial cohort) and similar patients treated with front-linechemotherapy as part of routine clinical care (observational comparativecohort). We illustrate the process to define the estimand attributes and selectthe estimator to estimate the estimand of interest while accounting for keybaseline confounders, index date, and receipt of subsequent therapies. Theproposed combined framework provides more clarity on the causal contrast ofinterest and the estimator to adopt and thus facilitates design andinterpretation of the analyses.

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