The Target Trial Framework for Causal Inference From Observational Data: Why and When Is It Helpful?

Abstract

When randomized trials are not available to answer a causal question about the comparative effectiveness or safety of interventions, causal inferences are drawn using observational data. A helpful 2-step framework for causal inference from observational data is 1) specifying the protocol of the hypothetical randomized pragmatic trial that would answer the causal question of interest (the target trial), and 2) using the observational data to attempt to emulate that trial. The target trial framework can improve the quality of observational analyses by preventing some common biases. In this article, we discuss the utility and scope of applications of the framework. We clarify that target trial emulation resolves problems related to incorrect design but not those related to data limitations. We also describe some settings in which adopting this approach is advantageous to generate effect estimates that can close the gaps that randomized trials have not filled. In these settings, the target trial framework helps reduce the ambiguity of causal questions.

Figure.. Example of specification and emulation of a target trial with the emulation based on existing data.

The causal contrasts are the effect of assignment (with conditional exchangeability as the identification assumption) and the per protocol effect. The identifying assumptions are the assumptions that are needed to identify the effect even if the sample size were infinite. * Except willingness to participate in an experiment, which in practice is an eligibility criterion for the target trial but not for its emulation. † For example, treatment prescription or dispensation. ‡ Incorporating competing events, when appropriate. The causal estimand implicitly includes no loss to follow-up and therefore the assumption that no loss to follow-up corresponds to a sufficiently well-defined intervention. § No need for adjustment is expected, but adjustment may be justified in the presence of random imbalances in baseline factors. || The assumption that the baseline confounders are known and measured.