Ideal trials, target trials and actual randomized trials

Causal inference is the goal of randomized controlled trials and many observational studies. The first step in a formal approach to causal inference is to define the estimand of interest, and in both types of study this can be intuitively defined as the effect in an ideal trial: a hypothetical perfect randomized experiment (with representative sample, perfect adherence, etc.). The target trial framework is an increasingly popular approach to causal inference in observational studies, but clarity is lacking in how a target trial should be specified and, crucially, how it relates to the ideal trial. In this paper, we consider these questions and use an example from respiratory epidemiology to highlight challenges with an approach that is commonly seen in applications: to specify a target trial in a way that is closely aligned to the observational study (e.g. uses the same eligibility criteria, outcome measure, etc.). The main issue is that such a target trial generally deviates from the ideal trial. Thus, even if the target trial can be emulated perfectly apart from randomization, biases beyond baseline confounding are likely to remain, relative to the estimand of interest. Without consideration of the ideal trial, these biases may go unnoticed, mirroring the often-overlooked biases of actual trials. Therefore, we suggest that, in both actual trials and observational studies, specifying the ideal trial and how the target or actual trial differs from it is necessary to systematically assess all potential sources of biases, and therefore appropriately design analyses and interpret findings.