The Ideal Trial: Defining Causal Estimands that Balance Relevance and Feasibility in Target Trial Emulations and Actual Randomized Trials

Causal inference is the goal of randomized trials and many observational studies. The first step in a formal causal inference framework is to define the causal estimand, and in both types of study this can be done mathematically or, equivalently, by specifying an ideal trial: a hypothetical perfect randomized experiment (with representative sample, perfect adherence, etc). The target trial framework is increasingly used for causal inference in observational studies, but clarity is lacking in how a target trial should be specified and how it relates to an ideal trial. Here, we review the mathematical and ideal trial approaches to defining a causal estimand, highlighting their equivalence and the need to balance practical relevance and feasibility of estimation regardless of approach. We then consider the question of how a target trial should be specified, outlining the challenges of a recommended approach, commonly seen in applications, that puts the focus heavily on the feasibility of estimation: to specify the target trial such that it is closely aligned with the observational data (e.g., uses the same eligibility criteria). We argue that with this "aligned" approach, biases may remain relative to the estimand of ultimate practical interest, defined by the ideal trial, that mirror the often-overlooked biases of actual trials. We conclude that consideration of the ideal trial and of how the target trial and its emulation or the actual trial differ from it is necessary to identify and manage all bias sources in both settings. An example from respiratory epidemiology is used for illustration.