Extending inferences from a randomized trial to a new target population

Understand

When treatment effect modifiers influence the decision to participate in a randomized trial, the average treatment effect in the population represented by the randomized individuals will differ from the effect in other populations.

• In this tutorial, we consider methods for extending causal inferences about time-fixed treatments from a trial to a new target population of non-participants, using data from a completed randomized trial and baseline covariate data from a sample from the target population.
• We examine methods based on modeling the expectation of the outcome, the probability of participation, or both (doubly robust).
• We compare the methods in a simulation study and show how they can be implemented in software.